Overview

Dataset statistics

Number of variables50
Number of observations101766
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory38.8 MiB
Average record size in memory400.0 B

Variable types

Numeric13
Categorical34
Boolean3

Alerts

examide has constant value "False"Constant
citoglipton has constant value "False"Constant
medical_specialty has a high cardinality: 73 distinct valuesHigh cardinality
diag_1 has a high cardinality: 717 distinct valuesHigh cardinality
diag_2 has a high cardinality: 749 distinct valuesHigh cardinality
diag_3 has a high cardinality: 790 distinct valuesHigh cardinality
encounter_id is highly overall correlated with patient_nbrHigh correlation
patient_nbr is highly overall correlated with encounter_idHigh correlation
insulin is highly overall correlated with change and 1 other fieldsHigh correlation
change is highly overall correlated with insulin and 1 other fieldsHigh correlation
diabetesMed is highly overall correlated with insulin and 1 other fieldsHigh correlation
race is highly imbalanced (55.9%)Imbalance
weight is highly imbalanced (92.0%)Imbalance
medical_specialty is highly imbalanced (54.2%)Imbalance
max_glu_serum is highly imbalanced (81.2%)Imbalance
A1Cresult is highly imbalanced (54.8%)Imbalance
metformin is highly imbalanced (59.5%)Imbalance
repaglinide is highly imbalanced (93.9%)Imbalance
nateglinide is highly imbalanced (96.9%)Imbalance
chlorpropamide is highly imbalanced (99.5%)Imbalance
glimepiride is highly imbalanced (84.0%)Imbalance
acetohexamide is highly imbalanced (> 99.9%)Imbalance
glipizide is highly imbalanced (69.2%)Imbalance
glyburide is highly imbalanced (72.3%)Imbalance
tolbutamide is highly imbalanced (99.7%)Imbalance
pioglitazone is highly imbalanced (80.2%)Imbalance
rosiglitazone is highly imbalanced (82.2%)Imbalance
acarbose is highly imbalanced (98.5%)Imbalance
miglitol is highly imbalanced (99.7%)Imbalance
troglitazone is highly imbalanced (> 99.9%)Imbalance
tolazamide is highly imbalanced (99.7%)Imbalance
glyburide-metformin is highly imbalanced (97.0%)Imbalance
glipizide-metformin is highly imbalanced (99.8%)Imbalance
glimepiride-pioglitazone is highly imbalanced (> 99.9%)Imbalance
metformin-rosiglitazone is highly imbalanced (> 99.9%)Imbalance
metformin-pioglitazone is highly imbalanced (> 99.9%)Imbalance
number_emergency is highly skewed (γ1 = 22.85558215)Skewed
encounter_id has unique valuesUnique
num_procedures has 46652 (45.8%) zerosZeros
number_outpatient has 85027 (83.6%) zerosZeros
number_emergency has 90383 (88.8%) zerosZeros
number_inpatient has 67630 (66.5%) zerosZeros

Reproduction

Analysis started2023-03-28 04:58:42.791942
Analysis finished2023-03-28 05:00:27.416861
Duration1 minute and 44.62 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

encounter_id
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct101766
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6520165 × 108
Minimum12522
Maximum4.4386722 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size795.2 KiB
2023-03-28T10:30:27.595710image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum12522
5-th percentile27170784
Q184961194
median1.5238899 × 108
Q32.3027089 × 108
95-th percentile3.7896284 × 108
Maximum4.4386722 × 108
Range4.438547 × 108
Interquartile range (IQR)1.4530969 × 108

Descriptive statistics

Standard deviation1.026403 × 108
Coefficient of variation (CV)0.62130311
Kurtosis-0.10207139
Mean1.6520165 × 108
Median Absolute Deviation (MAD)70921143
Skewness0.69914155
Sum1.6811911 × 1013
Variance1.053503 × 1016
MonotonicityNot monotonic
2023-03-28T10:30:27.794816image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2278392 1
 
< 0.1%
190792044 1
 
< 0.1%
190790070 1
 
< 0.1%
190789722 1
 
< 0.1%
190786806 1
 
< 0.1%
190785018 1
 
< 0.1%
190781412 1
 
< 0.1%
190775886 1
 
< 0.1%
190764504 1
 
< 0.1%
190760322 1
 
< 0.1%
Other values (101756) 101756
> 99.9%
ValueCountFrequency (%)
12522 1
< 0.1%
15738 1
< 0.1%
16680 1
< 0.1%
28236 1
< 0.1%
35754 1
< 0.1%
36900 1
< 0.1%
40926 1
< 0.1%
42570 1
< 0.1%
55842 1
< 0.1%
62256 1
< 0.1%
ValueCountFrequency (%)
443867222 1
< 0.1%
443857166 1
< 0.1%
443854148 1
< 0.1%
443847782 1
< 0.1%
443847548 1
< 0.1%
443847176 1
< 0.1%
443842778 1
< 0.1%
443842340 1
< 0.1%
443842136 1
< 0.1%
443842070 1
< 0.1%

patient_nbr
Real number (ℝ)

Distinct71518
Distinct (%)70.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54330401
Minimum135
Maximum1.8950262 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size795.2 KiB
2023-03-28T10:30:28.000571image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum135
5-th percentile1456971.8
Q123413221
median45505143
Q387545950
95-th percentile1.1148027 × 108
Maximum1.8950262 × 108
Range1.8950248 × 108
Interquartile range (IQR)64132729

Descriptive statistics

Standard deviation38696359
Coefficient of variation (CV)0.71224138
Kurtosis-0.34737204
Mean54330401
Median Absolute Deviation (MAD)32950134
Skewness0.47128072
Sum5.5289876 × 1012
Variance1.4974082 × 1015
MonotonicityNot monotonic
2023-03-28T10:30:28.213431image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
88785891 40
 
< 0.1%
43140906 28
 
< 0.1%
1660293 23
 
< 0.1%
88227540 23
 
< 0.1%
23199021 23
 
< 0.1%
23643405 22
 
< 0.1%
84428613 22
 
< 0.1%
92709351 21
 
< 0.1%
88789707 20
 
< 0.1%
29903877 20
 
< 0.1%
Other values (71508) 101524
99.8%
ValueCountFrequency (%)
135 2
 
< 0.1%
378 1
 
< 0.1%
729 1
 
< 0.1%
774 1
 
< 0.1%
927 1
 
< 0.1%
1152 5
< 0.1%
1305 1
 
< 0.1%
1314 3
< 0.1%
1629 1
 
< 0.1%
2025 1
 
< 0.1%
ValueCountFrequency (%)
189502619 1
< 0.1%
189481478 1
< 0.1%
189445127 1
< 0.1%
189365864 1
< 0.1%
189351095 1
< 0.1%
189349430 1
< 0.1%
189332087 1
< 0.1%
189298877 1
< 0.1%
189257846 2
< 0.1%
189215762 1
< 0.1%

race
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
Caucasian
76099 
AfricanAmerican
19210 
?
 
2273
Hispanic
 
2037
Other
 
1506

Length

Max length15
Median length9
Mean length9.8495077
Min length1

Characters and Unicode

Total characters1002345
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCaucasian
2nd rowCaucasian
3rd rowAfricanAmerican
4th rowCaucasian
5th rowCaucasian

Common Values

ValueCountFrequency (%)
Caucasian 76099
74.8%
AfricanAmerican 19210
 
18.9%
? 2273
 
2.2%
Hispanic 2037
 
2.0%
Other 1506
 
1.5%
Asian 641
 
0.6%

Length

2023-03-28T10:30:28.567180image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-28T10:30:28.772829image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
caucasian 76099
74.8%
africanamerican 19210
 
18.9%
2273
 
2.2%
hispanic 2037
 
2.0%
other 1506
 
1.5%
asian 641
 
0.6%

Most occurring characters

ValueCountFrequency (%)
a 269395
26.9%
i 119234
11.9%
n 117197
11.7%
c 116556
11.6%
s 78777
 
7.9%
C 76099
 
7.6%
u 76099
 
7.6%
r 39926
 
4.0%
A 39061
 
3.9%
e 20716
 
2.1%
Other values (8) 49285
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 881369
87.9%
Uppercase Letter 118703
 
11.8%
Other Punctuation 2273
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 269395
30.6%
i 119234
13.5%
n 117197
13.3%
c 116556
13.2%
s 78777
 
8.9%
u 76099
 
8.6%
r 39926
 
4.5%
e 20716
 
2.4%
f 19210
 
2.2%
m 19210
 
2.2%
Other values (3) 5049
 
0.6%
Uppercase Letter
ValueCountFrequency (%)
C 76099
64.1%
A 39061
32.9%
H 2037
 
1.7%
O 1506
 
1.3%
Other Punctuation
ValueCountFrequency (%)
? 2273
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1000072
99.8%
Common 2273
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 269395
26.9%
i 119234
11.9%
n 117197
11.7%
c 116556
11.7%
s 78777
 
7.9%
C 76099
 
7.6%
u 76099
 
7.6%
r 39926
 
4.0%
A 39061
 
3.9%
e 20716
 
2.1%
Other values (7) 47012
 
4.7%
Common
ValueCountFrequency (%)
? 2273
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1002345
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 269395
26.9%
i 119234
11.9%
n 117197
11.7%
c 116556
11.6%
s 78777
 
7.9%
C 76099
 
7.6%
u 76099
 
7.6%
r 39926
 
4.0%
A 39061
 
3.9%
e 20716
 
2.1%
Other values (8) 49285
 
4.9%

gender
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
Female
54708 
Male
47055 
Unknown/Invalid
 
3

Length

Max length15
Median length6
Mean length5.0754967
Min length4

Characters and Unicode

Total characters516513
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowFemale
3rd rowFemale
4th rowMale
5th rowMale

Common Values

ValueCountFrequency (%)
Female 54708
53.8%
Male 47055
46.2%
Unknown/Invalid 3
 
< 0.1%

Length

2023-03-28T10:30:28.944614image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-28T10:30:29.105396image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
female 54708
53.8%
male 47055
46.2%
unknown/invalid 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 156471
30.3%
a 101766
19.7%
l 101766
19.7%
F 54708
 
10.6%
m 54708
 
10.6%
M 47055
 
9.1%
n 12
 
< 0.1%
U 3
 
< 0.1%
k 3
 
< 0.1%
o 3
 
< 0.1%
Other values (6) 18
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 414741
80.3%
Uppercase Letter 101769
 
19.7%
Other Punctuation 3
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 156471
37.7%
a 101766
24.5%
l 101766
24.5%
m 54708
 
13.2%
n 12
 
< 0.1%
k 3
 
< 0.1%
o 3
 
< 0.1%
w 3
 
< 0.1%
v 3
 
< 0.1%
i 3
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
F 54708
53.8%
M 47055
46.2%
U 3
 
< 0.1%
I 3
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
/ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 516510
> 99.9%
Common 3
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 156471
30.3%
a 101766
19.7%
l 101766
19.7%
F 54708
 
10.6%
m 54708
 
10.6%
M 47055
 
9.1%
n 12
 
< 0.1%
U 3
 
< 0.1%
k 3
 
< 0.1%
o 3
 
< 0.1%
Other values (5) 15
 
< 0.1%
Common
ValueCountFrequency (%)
/ 3
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 516513
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 156471
30.3%
a 101766
19.7%
l 101766
19.7%
F 54708
 
10.6%
m 54708
 
10.6%
M 47055
 
9.1%
n 12
 
< 0.1%
U 3
 
< 0.1%
k 3
 
< 0.1%
o 3
 
< 0.1%
Other values (6) 18
 
< 0.1%

age
Categorical

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
[70-80)
26068 
[60-70)
22483 
[50-60)
17256 
[80-90)
17197 
[40-50)
9685 
Other values (5)
9077 

Length

Max length8
Median length7
Mean length7.0258633
Min length6

Characters and Unicode

Total characters714994
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row[0-10)
2nd row[10-20)
3rd row[20-30)
4th row[30-40)
5th row[40-50)

Common Values

ValueCountFrequency (%)
[70-80) 26068
25.6%
[60-70) 22483
22.1%
[50-60) 17256
17.0%
[80-90) 17197
16.9%
[40-50) 9685
 
9.5%
[30-40) 3775
 
3.7%
[90-100) 2793
 
2.7%
[20-30) 1657
 
1.6%
[10-20) 691
 
0.7%
[0-10) 161
 
0.2%

Length

2023-03-28T10:30:29.247358image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-28T10:30:29.462606image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
70-80 26068
25.6%
60-70 22483
22.1%
50-60 17256
17.0%
80-90 17197
16.9%
40-50 9685
 
9.5%
30-40 3775
 
3.7%
90-100 2793
 
2.7%
20-30 1657
 
1.6%
10-20 691
 
0.7%
0-10 161
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 206325
28.9%
[ 101766
14.2%
- 101766
14.2%
) 101766
14.2%
7 48551
 
6.8%
8 43265
 
6.1%
6 39739
 
5.6%
5 26941
 
3.8%
9 19990
 
2.8%
4 13460
 
1.9%
Other values (3) 11425
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 409696
57.3%
Open Punctuation 101766
 
14.2%
Dash Punctuation 101766
 
14.2%
Close Punctuation 101766
 
14.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 206325
50.4%
7 48551
 
11.9%
8 43265
 
10.6%
6 39739
 
9.7%
5 26941
 
6.6%
9 19990
 
4.9%
4 13460
 
3.3%
3 5432
 
1.3%
1 3645
 
0.9%
2 2348
 
0.6%
Open Punctuation
ValueCountFrequency (%)
[ 101766
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 101766
100.0%
Close Punctuation
ValueCountFrequency (%)
) 101766
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 714994
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 206325
28.9%
[ 101766
14.2%
- 101766
14.2%
) 101766
14.2%
7 48551
 
6.8%
8 43265
 
6.1%
6 39739
 
5.6%
5 26941
 
3.8%
9 19990
 
2.8%
4 13460
 
1.9%
Other values (3) 11425
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 714994
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 206325
28.9%
[ 101766
14.2%
- 101766
14.2%
) 101766
14.2%
7 48551
 
6.8%
8 43265
 
6.1%
6 39739
 
5.6%
5 26941
 
3.8%
9 19990
 
2.8%
4 13460
 
1.9%
Other values (3) 11425
 
1.6%

weight
Categorical

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
?
98569 
[75-100)
 
1336
[50-75)
 
897
[100-125)
 
625
[125-150)
 
145
Other values (5)
 
194

Length

Max length9
Median length1
Mean length1.2170961
Min length1

Characters and Unicode

Total characters123859
Distinct characters10
Distinct categories6 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row?
2nd row?
3rd row?
4th row?
5th row?

Common Values

ValueCountFrequency (%)
? 98569
96.9%
[75-100) 1336
 
1.3%
[50-75) 897
 
0.9%
[100-125) 625
 
0.6%
[125-150) 145
 
0.1%
[25-50) 97
 
0.1%
[0-25) 48
 
< 0.1%
[150-175) 35
 
< 0.1%
[175-200) 11
 
< 0.1%
>200 3
 
< 0.1%

Length

2023-03-28T10:30:29.670529image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-28T10:30:29.870608image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
98569
96.9%
75-100 1336
 
1.3%
50-75 897
 
0.9%
100-125 625
 
0.6%
125-150 145
 
0.1%
25-50 97
 
0.1%
0-25 48
 
< 0.1%
150-175 35
 
< 0.1%
175-200 11
 
< 0.1%
200 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
? 98569
79.6%
0 5172
 
4.2%
5 4368
 
3.5%
[ 3194
 
2.6%
- 3194
 
2.6%
) 3194
 
2.6%
1 2957
 
2.4%
7 2279
 
1.8%
2 929
 
0.8%
> 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 98569
79.6%
Decimal Number 15705
 
12.7%
Open Punctuation 3194
 
2.6%
Dash Punctuation 3194
 
2.6%
Close Punctuation 3194
 
2.6%
Math Symbol 3
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5172
32.9%
5 4368
27.8%
1 2957
18.8%
7 2279
14.5%
2 929
 
5.9%
Other Punctuation
ValueCountFrequency (%)
? 98569
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 3194
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3194
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3194
100.0%
Math Symbol
ValueCountFrequency (%)
> 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 123859
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
? 98569
79.6%
0 5172
 
4.2%
5 4368
 
3.5%
[ 3194
 
2.6%
- 3194
 
2.6%
) 3194
 
2.6%
1 2957
 
2.4%
7 2279
 
1.8%
2 929
 
0.8%
> 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 123859
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
? 98569
79.6%
0 5172
 
4.2%
5 4368
 
3.5%
[ 3194
 
2.6%
- 3194
 
2.6%
) 3194
 
2.6%
1 2957
 
2.4%
7 2279
 
1.8%
2 929
 
0.8%
> 3
 
< 0.1%

admission_type_id
Real number (ℝ)

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0240061
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size795.2 KiB
2023-03-28T10:30:30.054564image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33
95-th percentile6
Maximum8
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4454028
Coefficient of variation (CV)0.7141297
Kurtosis1.9424761
Mean2.0240061
Median Absolute Deviation (MAD)0
Skewness1.5919843
Sum205975
Variance2.0891893
MonotonicityNot monotonic
2023-03-28T10:30:30.180859image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 53990
53.1%
3 18869
 
18.5%
2 18480
 
18.2%
6 5291
 
5.2%
5 4785
 
4.7%
8 320
 
0.3%
7 21
 
< 0.1%
4 10
 
< 0.1%
ValueCountFrequency (%)
1 53990
53.1%
2 18480
 
18.2%
3 18869
 
18.5%
4 10
 
< 0.1%
5 4785
 
4.7%
6 5291
 
5.2%
7 21
 
< 0.1%
8 320
 
0.3%
ValueCountFrequency (%)
8 320
 
0.3%
7 21
 
< 0.1%
6 5291
 
5.2%
5 4785
 
4.7%
4 10
 
< 0.1%
3 18869
 
18.5%
2 18480
 
18.2%
1 53990
53.1%

discharge_disposition_id
Real number (ℝ)

Distinct26
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7156418
Minimum1
Maximum28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size795.2 KiB
2023-03-28T10:30:30.342572image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q34
95-th percentile18
Maximum28
Range27
Interquartile range (IQR)3

Descriptive statistics

Standard deviation5.2801655
Coefficient of variation (CV)1.4210642
Kurtosis6.0033468
Mean3.7156418
Median Absolute Deviation (MAD)0
Skewness2.563067
Sum378126
Variance27.880148
MonotonicityNot monotonic
2023-03-28T10:30:30.511502image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
1 60234
59.2%
3 13954
 
13.7%
6 12902
 
12.7%
18 3691
 
3.6%
2 2128
 
2.1%
22 1993
 
2.0%
11 1642
 
1.6%
5 1184
 
1.2%
25 989
 
1.0%
4 815
 
0.8%
Other values (16) 2234
 
2.2%
ValueCountFrequency (%)
1 60234
59.2%
2 2128
 
2.1%
3 13954
 
13.7%
4 815
 
0.8%
5 1184
 
1.2%
6 12902
 
12.7%
7 623
 
0.6%
8 108
 
0.1%
9 21
 
< 0.1%
10 6
 
< 0.1%
ValueCountFrequency (%)
28 139
 
0.1%
27 5
 
< 0.1%
25 989
 
1.0%
24 48
 
< 0.1%
23 412
 
0.4%
22 1993
2.0%
20 2
 
< 0.1%
19 8
 
< 0.1%
18 3691
3.6%
17 14
 
< 0.1%

admission_source_id
Real number (ℝ)

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7544366
Minimum1
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size795.2 KiB
2023-03-28T10:30:30.680847image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median7
Q37
95-th percentile17
Maximum25
Range24
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.0640808
Coefficient of variation (CV)0.70625173
Kurtosis1.7449894
Mean5.7544366
Median Absolute Deviation (MAD)0
Skewness1.0299349
Sum585606
Variance16.516753
MonotonicityNot monotonic
2023-03-28T10:30:30.844142image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
7 57494
56.5%
1 29565
29.1%
17 6781
 
6.7%
4 3187
 
3.1%
6 2264
 
2.2%
2 1104
 
1.1%
5 855
 
0.8%
3 187
 
0.2%
20 161
 
0.2%
9 125
 
0.1%
Other values (7) 43
 
< 0.1%
ValueCountFrequency (%)
1 29565
29.1%
2 1104
 
1.1%
3 187
 
0.2%
4 3187
 
3.1%
5 855
 
0.8%
6 2264
 
2.2%
7 57494
56.5%
8 16
 
< 0.1%
9 125
 
0.1%
10 8
 
< 0.1%
ValueCountFrequency (%)
25 2
 
< 0.1%
22 12
 
< 0.1%
20 161
 
0.2%
17 6781
6.7%
14 2
 
< 0.1%
13 1
 
< 0.1%
11 2
 
< 0.1%
10 8
 
< 0.1%
9 125
 
0.1%
8 16
 
< 0.1%

time_in_hospital
Real number (ℝ)

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3959869
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size795.2 KiB
2023-03-28T10:30:30.992380image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile11
Maximum14
Range13
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.9851078
Coefficient of variation (CV)0.67905293
Kurtosis0.85025084
Mean4.3959869
Median Absolute Deviation (MAD)2
Skewness1.1339987
Sum447362
Variance8.9108684
MonotonicityNot monotonic
2023-03-28T10:30:31.137050image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
3 17756
17.4%
2 17224
16.9%
1 14208
14.0%
4 13924
13.7%
5 9966
9.8%
6 7539
7.4%
7 5859
 
5.8%
8 4391
 
4.3%
9 3002
 
2.9%
10 2342
 
2.3%
Other values (4) 5555
 
5.5%
ValueCountFrequency (%)
1 14208
14.0%
2 17224
16.9%
3 17756
17.4%
4 13924
13.7%
5 9966
9.8%
6 7539
7.4%
7 5859
 
5.8%
8 4391
 
4.3%
9 3002
 
2.9%
10 2342
 
2.3%
ValueCountFrequency (%)
14 1042
 
1.0%
13 1210
 
1.2%
12 1448
 
1.4%
11 1855
 
1.8%
10 2342
 
2.3%
9 3002
 
2.9%
8 4391
4.3%
7 5859
5.8%
6 7539
7.4%
5 9966
9.8%

payer_code
Categorical

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
?
40256 
MC
32439 
HM
6274 
SP
5007 
BC
4655 
Other values (13)
13135 

Length

Max length2
Median length2
Mean length1.6044258
Min length1

Characters and Unicode

Total characters163276
Distinct characters17
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row?
2nd row?
3rd row?
4th row?
5th row?

Common Values

ValueCountFrequency (%)
? 40256
39.6%
MC 32439
31.9%
HM 6274
 
6.2%
SP 5007
 
4.9%
BC 4655
 
4.6%
MD 3532
 
3.5%
CP 2533
 
2.5%
UN 2448
 
2.4%
CM 1937
 
1.9%
OG 1033
 
1.0%
Other values (8) 1652
 
1.6%

Length

2023-03-28T10:30:31.303680image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
40256
39.6%
mc 32439
31.9%
hm 6274
 
6.2%
sp 5007
 
4.9%
bc 4655
 
4.6%
md 3532
 
3.5%
cp 2533
 
2.5%
un 2448
 
2.4%
cm 1937
 
1.9%
og 1033
 
1.0%
Other values (8) 1652
 
1.6%

Most occurring characters

ValueCountFrequency (%)
M 44810
27.4%
C 41845
25.6%
? 40256
24.7%
P 8211
 
5.0%
H 6420
 
3.9%
S 5062
 
3.1%
B 4655
 
2.9%
D 4081
 
2.5%
N 2448
 
1.5%
U 2448
 
1.5%
Other values (7) 3040
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 123020
75.3%
Other Punctuation 40256
 
24.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M 44810
36.4%
C 41845
34.0%
P 8211
 
6.7%
H 6420
 
5.2%
S 5062
 
4.1%
B 4655
 
3.8%
D 4081
 
3.3%
N 2448
 
2.0%
U 2448
 
2.0%
O 1720
 
1.4%
Other values (6) 1320
 
1.1%
Other Punctuation
ValueCountFrequency (%)
? 40256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 123020
75.3%
Common 40256
 
24.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 44810
36.4%
C 41845
34.0%
P 8211
 
6.7%
H 6420
 
5.2%
S 5062
 
4.1%
B 4655
 
3.8%
D 4081
 
3.3%
N 2448
 
2.0%
U 2448
 
2.0%
O 1720
 
1.4%
Other values (6) 1320
 
1.1%
Common
ValueCountFrequency (%)
? 40256
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 163276
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M 44810
27.4%
C 41845
25.6%
? 40256
24.7%
P 8211
 
5.0%
H 6420
 
3.9%
S 5062
 
3.1%
B 4655
 
2.9%
D 4081
 
2.5%
N 2448
 
1.5%
U 2448
 
1.5%
Other values (7) 3040
 
1.9%

medical_specialty
Categorical

HIGH CARDINALITY  IMBALANCE 

Distinct73
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
?
49949 
InternalMedicine
14635 
Emergency/Trauma
7565 
Family/GeneralPractice
7440 
Cardiology
5352 
Other values (68)
16825 

Length

Max length36
Median length33
Mean length8.6126702
Min length1

Characters and Unicode

Total characters876477
Distinct characters44
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)< 0.1%

Sample

1st rowPediatrics-Endocrinology
2nd row?
3rd row?
4th row?
5th row?

Common Values

ValueCountFrequency (%)
? 49949
49.1%
InternalMedicine 14635
 
14.4%
Emergency/Trauma 7565
 
7.4%
Family/GeneralPractice 7440
 
7.3%
Cardiology 5352
 
5.3%
Surgery-General 3099
 
3.0%
Nephrology 1613
 
1.6%
Orthopedics 1400
 
1.4%
Orthopedics-Reconstructive 1233
 
1.2%
Radiologist 1140
 
1.1%
Other values (63) 8340
 
8.2%

Length

2023-03-28T10:30:31.488677image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
49949
49.1%
internalmedicine 14635
 
14.4%
emergency/trauma 7565
 
7.4%
family/generalpractice 7440
 
7.3%
cardiology 5352
 
5.3%
surgery-general 3099
 
3.0%
nephrology 1613
 
1.6%
orthopedics 1400
 
1.4%
orthopedics-reconstructive 1233
 
1.2%
radiologist 1140
 
1.1%
Other values (63) 8340
 
8.2%

Most occurring characters

ValueCountFrequency (%)
e 105151
 
12.0%
r 76899
 
8.8%
a 71149
 
8.1%
n 68798
 
7.8%
i 63308
 
7.2%
c 50007
 
5.7%
? 49949
 
5.7%
l 48871
 
5.6%
y 34937
 
4.0%
t 34149
 
3.9%
Other values (34) 273259
31.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 705846
80.5%
Uppercase Letter 98148
 
11.2%
Other Punctuation 65856
 
7.5%
Dash Punctuation 6627
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 105151
14.9%
r 76899
10.9%
a 71149
10.1%
n 68798
9.7%
i 63308
9.0%
c 50007
7.1%
l 48871
6.9%
y 34937
 
4.9%
t 34149
 
4.8%
o 34053
 
4.8%
Other values (13) 118524
16.8%
Uppercase Letter
ValueCountFrequency (%)
M 15055
15.3%
I 14683
15.0%
G 11882
12.1%
P 10448
10.6%
T 8332
8.5%
E 7861
8.0%
F 7451
7.6%
C 6307
6.4%
S 5156
 
5.3%
O 4146
 
4.2%
Other values (7) 6827
7.0%
Other Punctuation
ValueCountFrequency (%)
? 49949
75.8%
/ 15871
 
24.1%
& 36
 
0.1%
Dash Punctuation
ValueCountFrequency (%)
- 6627
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 803994
91.7%
Common 72483
 
8.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 105151
13.1%
r 76899
 
9.6%
a 71149
 
8.8%
n 68798
 
8.6%
i 63308
 
7.9%
c 50007
 
6.2%
l 48871
 
6.1%
y 34937
 
4.3%
t 34149
 
4.2%
o 34053
 
4.2%
Other values (30) 216672
26.9%
Common
ValueCountFrequency (%)
? 49949
68.9%
/ 15871
 
21.9%
- 6627
 
9.1%
& 36
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 876477
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 105151
 
12.0%
r 76899
 
8.8%
a 71149
 
8.1%
n 68798
 
7.8%
i 63308
 
7.2%
c 50007
 
5.7%
? 49949
 
5.7%
l 48871
 
5.6%
y 34937
 
4.0%
t 34149
 
3.9%
Other values (34) 273259
31.2%

num_lab_procedures
Real number (ℝ)

Distinct118
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.095641
Minimum1
Maximum132
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size795.2 KiB
2023-03-28T10:30:31.703961image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q131
median44
Q357
95-th percentile73
Maximum132
Range131
Interquartile range (IQR)26

Descriptive statistics

Standard deviation19.674362
Coefficient of variation (CV)0.45652789
Kurtosis-0.24507352
Mean43.095641
Median Absolute Deviation (MAD)13
Skewness-0.23654392
Sum4385671
Variance387.08053
MonotonicityNot monotonic
2023-03-28T10:30:32.193439image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 3208
 
3.2%
43 2804
 
2.8%
44 2496
 
2.5%
45 2376
 
2.3%
38 2213
 
2.2%
40 2201
 
2.2%
46 2189
 
2.2%
41 2117
 
2.1%
42 2113
 
2.1%
47 2106
 
2.1%
Other values (108) 77943
76.6%
ValueCountFrequency (%)
1 3208
3.2%
2 1101
 
1.1%
3 668
 
0.7%
4 378
 
0.4%
5 286
 
0.3%
6 282
 
0.3%
7 323
 
0.3%
8 366
 
0.4%
9 933
 
0.9%
10 838
 
0.8%
ValueCountFrequency (%)
132 1
 
< 0.1%
129 1
 
< 0.1%
126 1
 
< 0.1%
121 1
 
< 0.1%
120 1
 
< 0.1%
118 1
 
< 0.1%
114 2
< 0.1%
113 3
< 0.1%
111 3
< 0.1%
109 4
< 0.1%

num_procedures
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3397304
Minimum0
Maximum6
Zeros46652
Zeros (%)45.8%
Negative0
Negative (%)0.0%
Memory size795.2 KiB
2023-03-28T10:30:32.483656image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.705807
Coefficient of variation (CV)1.2732465
Kurtosis0.8571103
Mean1.3397304
Median Absolute Deviation (MAD)1
Skewness1.3164148
Sum136339
Variance2.9097775
MonotonicityNot monotonic
2023-03-28T10:30:32.734734image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 46652
45.8%
1 20742
20.4%
2 12717
 
12.5%
3 9443
 
9.3%
6 4954
 
4.9%
4 4180
 
4.1%
5 3078
 
3.0%
ValueCountFrequency (%)
0 46652
45.8%
1 20742
20.4%
2 12717
 
12.5%
3 9443
 
9.3%
4 4180
 
4.1%
5 3078
 
3.0%
6 4954
 
4.9%
ValueCountFrequency (%)
6 4954
 
4.9%
5 3078
 
3.0%
4 4180
 
4.1%
3 9443
 
9.3%
2 12717
 
12.5%
1 20742
20.4%
0 46652
45.8%

num_medications
Real number (ℝ)

Distinct75
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.021844
Minimum1
Maximum81
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size795.2 KiB
2023-03-28T10:30:32.973818image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q110
median15
Q320
95-th percentile31
Maximum81
Range80
Interquartile range (IQR)10

Descriptive statistics

Standard deviation8.1275662
Coefficient of variation (CV)0.50728032
Kurtosis3.4681549
Mean16.021844
Median Absolute Deviation (MAD)5
Skewness1.3266721
Sum1630479
Variance66.057332
MonotonicityNot monotonic
2023-03-28T10:30:33.223912image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 6086
 
6.0%
12 6004
 
5.9%
11 5795
 
5.7%
15 5792
 
5.7%
14 5707
 
5.6%
16 5430
 
5.3%
10 5346
 
5.3%
17 4919
 
4.8%
9 4913
 
4.8%
18 4523
 
4.4%
Other values (65) 47251
46.4%
ValueCountFrequency (%)
1 262
 
0.3%
2 470
 
0.5%
3 900
 
0.9%
4 1417
 
1.4%
5 2017
 
2.0%
6 2699
2.7%
7 3484
3.4%
8 4353
4.3%
9 4913
4.8%
10 5346
5.3%
ValueCountFrequency (%)
81 1
 
< 0.1%
79 1
 
< 0.1%
75 2
 
< 0.1%
74 1
 
< 0.1%
72 3
< 0.1%
70 2
 
< 0.1%
69 5
< 0.1%
68 7
< 0.1%
67 7
< 0.1%
66 5
< 0.1%

number_outpatient
Real number (ℝ)

Distinct39
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.36935715
Minimum0
Maximum42
Zeros85027
Zeros (%)83.6%
Negative0
Negative (%)0.0%
Memory size795.2 KiB
2023-03-28T10:30:33.643060image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum42
Range42
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.2672651
Coefficient of variation (CV)3.4310019
Kurtosis147.90774
Mean0.36935715
Median Absolute Deviation (MAD)0
Skewness8.8329589
Sum37588
Variance1.6059608
MonotonicityNot monotonic
2023-03-28T10:30:33.818899image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
0 85027
83.6%
1 8547
 
8.4%
2 3594
 
3.5%
3 2042
 
2.0%
4 1099
 
1.1%
5 533
 
0.5%
6 303
 
0.3%
7 155
 
0.2%
8 98
 
0.1%
9 83
 
0.1%
Other values (29) 285
 
0.3%
ValueCountFrequency (%)
0 85027
83.6%
1 8547
 
8.4%
2 3594
 
3.5%
3 2042
 
2.0%
4 1099
 
1.1%
5 533
 
0.5%
6 303
 
0.3%
7 155
 
0.2%
8 98
 
0.1%
9 83
 
0.1%
ValueCountFrequency (%)
42 1
< 0.1%
40 1
< 0.1%
39 1
< 0.1%
38 1
< 0.1%
37 1
< 0.1%
36 2
< 0.1%
35 2
< 0.1%
34 1
< 0.1%
33 2
< 0.1%
29 2
< 0.1%

number_emergency
Real number (ℝ)

SKEWED  ZEROS 

Distinct33
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.19783621
Minimum0
Maximum76
Zeros90383
Zeros (%)88.8%
Negative0
Negative (%)0.0%
Memory size795.2 KiB
2023-03-28T10:30:33.994843image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum76
Range76
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.93047227
Coefficient of variation (CV)4.7032455
Kurtosis1191.6867
Mean0.19783621
Median Absolute Deviation (MAD)0
Skewness22.855582
Sum20133
Variance0.86577864
MonotonicityNot monotonic
2023-03-28T10:30:34.176440image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0 90383
88.8%
1 7677
 
7.5%
2 2042
 
2.0%
3 725
 
0.7%
4 374
 
0.4%
5 192
 
0.2%
6 94
 
0.1%
7 73
 
0.1%
8 50
 
< 0.1%
10 34
 
< 0.1%
Other values (23) 122
 
0.1%
ValueCountFrequency (%)
0 90383
88.8%
1 7677
 
7.5%
2 2042
 
2.0%
3 725
 
0.7%
4 374
 
0.4%
5 192
 
0.2%
6 94
 
0.1%
7 73
 
0.1%
8 50
 
< 0.1%
9 33
 
< 0.1%
ValueCountFrequency (%)
76 1
< 0.1%
64 1
< 0.1%
63 1
< 0.1%
54 1
< 0.1%
46 1
< 0.1%
42 1
< 0.1%
37 1
< 0.1%
29 1
< 0.1%
28 1
< 0.1%
25 2
< 0.1%

number_inpatient
Real number (ℝ)

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.63556591
Minimum0
Maximum21
Zeros67630
Zeros (%)66.5%
Negative0
Negative (%)0.0%
Memory size795.2 KiB
2023-03-28T10:30:34.360077image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum21
Range21
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2628633
Coefficient of variation (CV)1.9869903
Kurtosis20.719397
Mean0.63556591
Median Absolute Deviation (MAD)0
Skewness3.614139
Sum64679
Variance1.5948237
MonotonicityNot monotonic
2023-03-28T10:30:34.504177image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 67630
66.5%
1 19521
 
19.2%
2 7566
 
7.4%
3 3411
 
3.4%
4 1622
 
1.6%
5 812
 
0.8%
6 480
 
0.5%
7 268
 
0.3%
8 151
 
0.1%
9 111
 
0.1%
Other values (11) 194
 
0.2%
ValueCountFrequency (%)
0 67630
66.5%
1 19521
 
19.2%
2 7566
 
7.4%
3 3411
 
3.4%
4 1622
 
1.6%
5 812
 
0.8%
6 480
 
0.5%
7 268
 
0.3%
8 151
 
0.1%
9 111
 
0.1%
ValueCountFrequency (%)
21 1
 
< 0.1%
19 2
 
< 0.1%
18 1
 
< 0.1%
17 1
 
< 0.1%
16 6
 
< 0.1%
15 9
 
< 0.1%
14 10
 
< 0.1%
13 20
< 0.1%
12 34
< 0.1%
11 49
< 0.1%

diag_1
Categorical

Distinct717
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
428
 
6862
414
 
6581
786
 
4016
410
 
3614
486
 
3508
Other values (712)
77185 

Length

Max length6
Median length3
Mean length3.1752157
Min length1

Characters and Unicode

Total characters323129
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique82 ?
Unique (%)0.1%

Sample

1st row250.83
2nd row276
3rd row648
4th row8
5th row197

Common Values

ValueCountFrequency (%)
428 6862
 
6.7%
414 6581
 
6.5%
786 4016
 
3.9%
410 3614
 
3.6%
486 3508
 
3.4%
427 2766
 
2.7%
491 2275
 
2.2%
715 2151
 
2.1%
682 2042
 
2.0%
434 2028
 
2.0%
Other values (707) 65923
64.8%

Length

2023-03-28T10:30:34.672231image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
428 6862
 
6.7%
414 6581
 
6.5%
786 4016
 
3.9%
410 3614
 
3.6%
486 3508
 
3.4%
427 2766
 
2.7%
491 2275
 
2.2%
715 2151
 
2.1%
682 2042
 
2.0%
434 2028
 
2.0%
Other values (707) 65923
64.8%

Most occurring characters

ValueCountFrequency (%)
4 55457
17.2%
2 39876
12.3%
8 37949
11.7%
5 37131
11.5%
7 28668
8.9%
1 28106
8.7%
0 24960
7.7%
6 23198
7.2%
9 19978
 
6.2%
3 17618
 
5.5%
Other values (4) 10188
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 312941
96.8%
Other Punctuation 8543
 
2.6%
Uppercase Letter 1645
 
0.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 55457
17.7%
2 39876
12.7%
8 37949
12.1%
5 37131
11.9%
7 28668
9.2%
1 28106
9.0%
0 24960
8.0%
6 23198
7.4%
9 19978
 
6.4%
3 17618
 
5.6%
Other Punctuation
ValueCountFrequency (%)
. 8522
99.8%
? 21
 
0.2%
Uppercase Letter
ValueCountFrequency (%)
V 1644
99.9%
E 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 321484
99.5%
Latin 1645
 
0.5%

Most frequent character per script

Common
ValueCountFrequency (%)
4 55457
17.3%
2 39876
12.4%
8 37949
11.8%
5 37131
11.5%
7 28668
8.9%
1 28106
8.7%
0 24960
7.8%
6 23198
7.2%
9 19978
 
6.2%
3 17618
 
5.5%
Other values (2) 8543
 
2.7%
Latin
ValueCountFrequency (%)
V 1644
99.9%
E 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 323129
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 55457
17.2%
2 39876
12.3%
8 37949
11.7%
5 37131
11.5%
7 28668
8.9%
1 28106
8.7%
0 24960
7.7%
6 23198
7.2%
9 19978
 
6.2%
3 17618
 
5.5%
Other values (4) 10188
 
3.2%

diag_2
Categorical

Distinct749
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
276
 
6752
428
 
6662
250
 
6071
427
 
5036
401
 
3736
Other values (744)
73509 

Length

Max length6
Median length3
Mean length3.166195
Min length1

Characters and Unicode

Total characters322211
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique124 ?
Unique (%)0.1%

Sample

1st row?
2nd row250.01
3rd row250
4th row250.43
5th row157

Common Values

ValueCountFrequency (%)
276 6752
 
6.6%
428 6662
 
6.5%
250 6071
 
6.0%
427 5036
 
4.9%
401 3736
 
3.7%
496 3305
 
3.2%
599 3288
 
3.2%
403 2823
 
2.8%
414 2650
 
2.6%
411 2566
 
2.5%
Other values (739) 58877
57.9%

Length

2023-03-28T10:30:34.824541image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
276 6752
 
6.6%
428 6662
 
6.5%
250 6071
 
6.0%
427 5036
 
4.9%
401 3736
 
3.7%
496 3305
 
3.2%
599 3288
 
3.2%
403 2823
 
2.8%
414 2650
 
2.6%
411 2566
 
2.5%
Other values (739) 58877
57.9%

Most occurring characters

ValueCountFrequency (%)
4 51155
15.9%
2 49765
15.4%
5 38176
11.8%
0 34046
10.6%
8 28711
8.9%
7 28654
8.9%
1 26158
8.1%
9 21842
6.8%
6 19990
 
6.2%
3 14097
 
4.4%
Other values (4) 9617
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 312594
97.0%
Other Punctuation 7081
 
2.2%
Uppercase Letter 2536
 
0.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 51155
16.4%
2 49765
15.9%
5 38176
12.2%
0 34046
10.9%
8 28711
9.2%
7 28654
9.2%
1 26158
8.4%
9 21842
7.0%
6 19990
 
6.4%
3 14097
 
4.5%
Other Punctuation
ValueCountFrequency (%)
. 6723
94.9%
? 358
 
5.1%
Uppercase Letter
ValueCountFrequency (%)
V 1805
71.2%
E 731
28.8%

Most occurring scripts

ValueCountFrequency (%)
Common 319675
99.2%
Latin 2536
 
0.8%

Most frequent character per script

Common
ValueCountFrequency (%)
4 51155
16.0%
2 49765
15.6%
5 38176
11.9%
0 34046
10.7%
8 28711
9.0%
7 28654
9.0%
1 26158
8.2%
9 21842
6.8%
6 19990
 
6.3%
3 14097
 
4.4%
Other values (2) 7081
 
2.2%
Latin
ValueCountFrequency (%)
V 1805
71.2%
E 731
28.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 322211
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 51155
15.9%
2 49765
15.4%
5 38176
11.8%
0 34046
10.6%
8 28711
8.9%
7 28654
8.9%
1 26158
8.1%
9 21842
6.8%
6 19990
 
6.2%
3 14097
 
4.4%
Other values (4) 9617
 
3.0%

diag_3
Categorical

Distinct790
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
250
11555 
401
8289 
276
 
5175
428
 
4577
427
 
3955
Other values (785)
68215 

Length

Max length6
Median length3
Mean length3.1116581
Min length1

Characters and Unicode

Total characters316661
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique122 ?
Unique (%)0.1%

Sample

1st row?
2nd row255
3rd rowV27
4th row403
5th row250

Common Values

ValueCountFrequency (%)
250 11555
 
11.4%
401 8289
 
8.1%
276 5175
 
5.1%
428 4577
 
4.5%
427 3955
 
3.9%
414 3664
 
3.6%
496 2605
 
2.6%
403 2357
 
2.3%
585 1992
 
2.0%
272 1969
 
1.9%
Other values (780) 55628
54.7%

Length

2023-03-28T10:30:34.977281image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
250 11555
 
11.4%
401 8289
 
8.1%
276 5175
 
5.1%
428 4577
 
4.5%
427 3955
 
3.9%
414 3664
 
3.6%
496 2605
 
2.6%
403 2357
 
2.3%
585 1992
 
2.0%
272 1969
 
1.9%
Other values (780) 55628
54.7%

Most occurring characters

ValueCountFrequency (%)
2 51244
16.2%
4 49252
15.6%
5 41260
13.0%
0 39711
12.5%
7 26504
8.4%
1 24684
7.8%
8 23825
7.5%
9 17323
 
5.5%
6 16441
 
5.2%
3 14333
 
4.5%
Other values (4) 12084
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 304577
96.2%
Other Punctuation 7026
 
2.2%
Uppercase Letter 5058
 
1.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 51244
16.8%
4 49252
16.2%
5 41260
13.5%
0 39711
13.0%
7 26504
8.7%
1 24684
8.1%
8 23825
7.8%
9 17323
 
5.7%
6 16441
 
5.4%
3 14333
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 5603
79.7%
? 1423
 
20.3%
Uppercase Letter
ValueCountFrequency (%)
V 3814
75.4%
E 1244
 
24.6%

Most occurring scripts

ValueCountFrequency (%)
Common 311603
98.4%
Latin 5058
 
1.6%

Most frequent character per script

Common
ValueCountFrequency (%)
2 51244
16.4%
4 49252
15.8%
5 41260
13.2%
0 39711
12.7%
7 26504
8.5%
1 24684
7.9%
8 23825
7.6%
9 17323
 
5.6%
6 16441
 
5.3%
3 14333
 
4.6%
Other values (2) 7026
 
2.3%
Latin
ValueCountFrequency (%)
V 3814
75.4%
E 1244
 
24.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 316661
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 51244
16.2%
4 49252
15.6%
5 41260
13.0%
0 39711
12.5%
7 26504
8.4%
1 24684
7.8%
8 23825
7.5%
9 17323
 
5.5%
6 16441
 
5.2%
3 14333
 
4.5%
Other values (4) 12084
 
3.8%

number_diagnoses
Real number (ℝ)

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.4226068
Minimum1
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size795.2 KiB
2023-03-28T10:30:35.131840image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q16
median8
Q39
95-th percentile9
Maximum16
Range15
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.9336001
Coefficient of variation (CV)0.26050149
Kurtosis-0.079056024
Mean7.4226068
Median Absolute Deviation (MAD)1
Skewness-0.87674624
Sum755369
Variance3.7388095
MonotonicityNot monotonic
2023-03-28T10:30:35.273781image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
9 49474
48.6%
5 11393
 
11.2%
8 10616
 
10.4%
7 10393
 
10.2%
6 10161
 
10.0%
4 5537
 
5.4%
3 2835
 
2.8%
2 1023
 
1.0%
1 219
 
0.2%
16 45
 
< 0.1%
Other values (6) 70
 
0.1%
ValueCountFrequency (%)
1 219
 
0.2%
2 1023
 
1.0%
3 2835
 
2.8%
4 5537
 
5.4%
5 11393
 
11.2%
6 10161
 
10.0%
7 10393
 
10.2%
8 10616
 
10.4%
9 49474
48.6%
10 17
 
< 0.1%
ValueCountFrequency (%)
16 45
 
< 0.1%
15 10
 
< 0.1%
14 7
 
< 0.1%
13 16
 
< 0.1%
12 9
 
< 0.1%
11 11
 
< 0.1%
10 17
 
< 0.1%
9 49474
48.6%
8 10616
 
10.4%
7 10393
 
10.2%

max_glu_serum
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
None
96420 
Norm
 
2597
>200
 
1485
>300
 
1264

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters407064
Distinct characters10
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNone
2nd rowNone
3rd rowNone
4th rowNone
5th rowNone

Common Values

ValueCountFrequency (%)
None 96420
94.7%
Norm 2597
 
2.6%
>200 1485
 
1.5%
>300 1264
 
1.2%

Length

2023-03-28T10:30:35.447163image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-28T10:30:35.644168image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
none 96420
94.7%
norm 2597
 
2.6%
200 1485
 
1.5%
300 1264
 
1.2%

Most occurring characters

ValueCountFrequency (%)
N 99017
24.3%
o 99017
24.3%
n 96420
23.7%
e 96420
23.7%
0 5498
 
1.4%
> 2749
 
0.7%
r 2597
 
0.6%
m 2597
 
0.6%
2 1485
 
0.4%
3 1264
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 297051
73.0%
Uppercase Letter 99017
 
24.3%
Decimal Number 8247
 
2.0%
Math Symbol 2749
 
0.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 99017
33.3%
n 96420
32.5%
e 96420
32.5%
r 2597
 
0.9%
m 2597
 
0.9%
Decimal Number
ValueCountFrequency (%)
0 5498
66.7%
2 1485
 
18.0%
3 1264
 
15.3%
Uppercase Letter
ValueCountFrequency (%)
N 99017
100.0%
Math Symbol
ValueCountFrequency (%)
> 2749
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 396068
97.3%
Common 10996
 
2.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 99017
25.0%
o 99017
25.0%
n 96420
24.3%
e 96420
24.3%
r 2597
 
0.7%
m 2597
 
0.7%
Common
ValueCountFrequency (%)
0 5498
50.0%
> 2749
25.0%
2 1485
 
13.5%
3 1264
 
11.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 407064
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 99017
24.3%
o 99017
24.3%
n 96420
23.7%
e 96420
23.7%
0 5498
 
1.4%
> 2749
 
0.7%
r 2597
 
0.6%
m 2597
 
0.6%
2 1485
 
0.4%
3 1264
 
0.3%

A1Cresult
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
None
84748 
>8
 
8216
Norm
 
4990
>7
 
3812

Length

Max length4
Median length4
Mean length3.7636146
Min length2

Characters and Unicode

Total characters383008
Distinct characters9
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNone
2nd rowNone
3rd rowNone
4th rowNone
5th rowNone

Common Values

ValueCountFrequency (%)
None 84748
83.3%
>8 8216
 
8.1%
Norm 4990
 
4.9%
>7 3812
 
3.7%

Length

2023-03-28T10:30:35.803449image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-28T10:30:35.966414image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
none 84748
83.3%
8 8216
 
8.1%
norm 4990
 
4.9%
7 3812
 
3.7%

Most occurring characters

ValueCountFrequency (%)
N 89738
23.4%
o 89738
23.4%
n 84748
22.1%
e 84748
22.1%
> 12028
 
3.1%
8 8216
 
2.1%
r 4990
 
1.3%
m 4990
 
1.3%
7 3812
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 269214
70.3%
Uppercase Letter 89738
 
23.4%
Math Symbol 12028
 
3.1%
Decimal Number 12028
 
3.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 89738
33.3%
n 84748
31.5%
e 84748
31.5%
r 4990
 
1.9%
m 4990
 
1.9%
Decimal Number
ValueCountFrequency (%)
8 8216
68.3%
7 3812
31.7%
Uppercase Letter
ValueCountFrequency (%)
N 89738
100.0%
Math Symbol
ValueCountFrequency (%)
> 12028
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 358952
93.7%
Common 24056
 
6.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 89738
25.0%
o 89738
25.0%
n 84748
23.6%
e 84748
23.6%
r 4990
 
1.4%
m 4990
 
1.4%
Common
ValueCountFrequency (%)
> 12028
50.0%
8 8216
34.2%
7 3812
 
15.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 383008
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 89738
23.4%
o 89738
23.4%
n 84748
22.1%
e 84748
22.1%
> 12028
 
3.1%
8 8216
 
2.1%
r 4990
 
1.3%
m 4990
 
1.3%
7 3812
 
1.0%

metformin
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
No
81778 
Steady
18346 
Up
 
1067
Down
 
575

Length

Max length6
Median length2
Mean length2.7324057
Min length2

Characters and Unicode

Total characters278066
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 81778
80.4%
Steady 18346
 
18.0%
Up 1067
 
1.0%
Down 575
 
0.6%

Length

2023-03-28T10:30:36.111150image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-28T10:30:36.302057image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
no 81778
80.4%
steady 18346
 
18.0%
up 1067
 
1.0%
down 575
 
0.6%

Most occurring characters

ValueCountFrequency (%)
o 82353
29.6%
N 81778
29.4%
S 18346
 
6.6%
t 18346
 
6.6%
e 18346
 
6.6%
a 18346
 
6.6%
d 18346
 
6.6%
y 18346
 
6.6%
U 1067
 
0.4%
p 1067
 
0.4%
Other values (3) 1725
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 176300
63.4%
Uppercase Letter 101766
36.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 82353
46.7%
t 18346
 
10.4%
e 18346
 
10.4%
a 18346
 
10.4%
d 18346
 
10.4%
y 18346
 
10.4%
p 1067
 
0.6%
w 575
 
0.3%
n 575
 
0.3%
Uppercase Letter
ValueCountFrequency (%)
N 81778
80.4%
S 18346
 
18.0%
U 1067
 
1.0%
D 575
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 278066
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 82353
29.6%
N 81778
29.4%
S 18346
 
6.6%
t 18346
 
6.6%
e 18346
 
6.6%
a 18346
 
6.6%
d 18346
 
6.6%
y 18346
 
6.6%
U 1067
 
0.4%
p 1067
 
0.4%
Other values (3) 1725
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 278066
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 82353
29.6%
N 81778
29.4%
S 18346
 
6.6%
t 18346
 
6.6%
e 18346
 
6.6%
a 18346
 
6.6%
d 18346
 
6.6%
y 18346
 
6.6%
U 1067
 
0.4%
p 1067
 
0.4%
Other values (3) 1725
 
0.6%

repaglinide
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
No
100227 
Steady
 
1384
Up
 
110
Down
 
45

Length

Max length6
Median length2
Mean length2.0552837
Min length2

Characters and Unicode

Total characters209158
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 100227
98.5%
Steady 1384
 
1.4%
Up 110
 
0.1%
Down 45
 
< 0.1%

Length

2023-03-28T10:30:36.453022image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-28T10:30:36.626376image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
no 100227
98.5%
steady 1384
 
1.4%
up 110
 
0.1%
down 45
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
o 100272
47.9%
N 100227
47.9%
S 1384
 
0.7%
t 1384
 
0.7%
e 1384
 
0.7%
a 1384
 
0.7%
d 1384
 
0.7%
y 1384
 
0.7%
U 110
 
0.1%
p 110
 
0.1%
Other values (3) 135
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 107392
51.3%
Uppercase Letter 101766
48.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 100272
93.4%
t 1384
 
1.3%
e 1384
 
1.3%
a 1384
 
1.3%
d 1384
 
1.3%
y 1384
 
1.3%
p 110
 
0.1%
w 45
 
< 0.1%
n 45
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
N 100227
98.5%
S 1384
 
1.4%
U 110
 
0.1%
D 45
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 209158
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 100272
47.9%
N 100227
47.9%
S 1384
 
0.7%
t 1384
 
0.7%
e 1384
 
0.7%
a 1384
 
0.7%
d 1384
 
0.7%
y 1384
 
0.7%
U 110
 
0.1%
p 110
 
0.1%
Other values (3) 135
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 209158
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 100272
47.9%
N 100227
47.9%
S 1384
 
0.7%
t 1384
 
0.7%
e 1384
 
0.7%
a 1384
 
0.7%
d 1384
 
0.7%
y 1384
 
0.7%
U 110
 
0.1%
p 110
 
0.1%
Other values (3) 135
 
0.1%

nateglinide
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
No
101063 
Steady
 
668
Up
 
24
Down
 
11

Length

Max length6
Median length2
Mean length2.0264725
Min length2

Characters and Unicode

Total characters206226
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 101063
99.3%
Steady 668
 
0.7%
Up 24
 
< 0.1%
Down 11
 
< 0.1%

Length

2023-03-28T10:30:36.772198image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-28T10:30:36.941439image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
no 101063
99.3%
steady 668
 
0.7%
up 24
 
< 0.1%
down 11
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
o 101074
49.0%
N 101063
49.0%
S 668
 
0.3%
t 668
 
0.3%
e 668
 
0.3%
a 668
 
0.3%
d 668
 
0.3%
y 668
 
0.3%
U 24
 
< 0.1%
p 24
 
< 0.1%
Other values (3) 33
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 104460
50.7%
Uppercase Letter 101766
49.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 101074
96.8%
t 668
 
0.6%
e 668
 
0.6%
a 668
 
0.6%
d 668
 
0.6%
y 668
 
0.6%
p 24
 
< 0.1%
w 11
 
< 0.1%
n 11
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
N 101063
99.3%
S 668
 
0.7%
U 24
 
< 0.1%
D 11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 206226
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 101074
49.0%
N 101063
49.0%
S 668
 
0.3%
t 668
 
0.3%
e 668
 
0.3%
a 668
 
0.3%
d 668
 
0.3%
y 668
 
0.3%
U 24
 
< 0.1%
p 24
 
< 0.1%
Other values (3) 33
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 206226
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 101074
49.0%
N 101063
49.0%
S 668
 
0.3%
t 668
 
0.3%
e 668
 
0.3%
a 668
 
0.3%
d 668
 
0.3%
y 668
 
0.3%
U 24
 
< 0.1%
p 24
 
< 0.1%
Other values (3) 33
 
< 0.1%

chlorpropamide
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
No
101680 
Steady
 
79
Up
 
6
Down
 
1

Length

Max length6
Median length2
Mean length2.0031248
Min length2

Characters and Unicode

Total characters203850
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 101680
99.9%
Steady 79
 
0.1%
Up 6
 
< 0.1%
Down 1
 
< 0.1%

Length

2023-03-28T10:30:37.213978image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-28T10:30:37.410205image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
no 101680
99.9%
steady 79
 
0.1%
up 6
 
< 0.1%
down 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
o 101681
49.9%
N 101680
49.9%
S 79
 
< 0.1%
t 79
 
< 0.1%
e 79
 
< 0.1%
a 79
 
< 0.1%
d 79
 
< 0.1%
y 79
 
< 0.1%
U 6
 
< 0.1%
p 6
 
< 0.1%
Other values (3) 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 102084
50.1%
Uppercase Letter 101766
49.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 101681
99.6%
t 79
 
0.1%
e 79
 
0.1%
a 79
 
0.1%
d 79
 
0.1%
y 79
 
0.1%
p 6
 
< 0.1%
w 1
 
< 0.1%
n 1
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
N 101680
99.9%
S 79
 
0.1%
U 6
 
< 0.1%
D 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 203850
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 101681
49.9%
N 101680
49.9%
S 79
 
< 0.1%
t 79
 
< 0.1%
e 79
 
< 0.1%
a 79
 
< 0.1%
d 79
 
< 0.1%
y 79
 
< 0.1%
U 6
 
< 0.1%
p 6
 
< 0.1%
Other values (3) 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 203850
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 101681
49.9%
N 101680
49.9%
S 79
 
< 0.1%
t 79
 
< 0.1%
e 79
 
< 0.1%
a 79
 
< 0.1%
d 79
 
< 0.1%
y 79
 
< 0.1%
U 6
 
< 0.1%
p 6
 
< 0.1%
Other values (3) 3
 
< 0.1%

glimepiride
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
No
96575 
Steady
 
4670
Up
 
327
Down
 
194

Length

Max length6
Median length2
Mean length2.187371
Min length2

Characters and Unicode

Total characters222600
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 96575
94.9%
Steady 4670
 
4.6%
Up 327
 
0.3%
Down 194
 
0.2%

Length

2023-03-28T10:30:37.570248image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-28T10:30:37.743278image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
no 96575
94.9%
steady 4670
 
4.6%
up 327
 
0.3%
down 194
 
0.2%

Most occurring characters

ValueCountFrequency (%)
o 96769
43.5%
N 96575
43.4%
S 4670
 
2.1%
t 4670
 
2.1%
e 4670
 
2.1%
a 4670
 
2.1%
d 4670
 
2.1%
y 4670
 
2.1%
U 327
 
0.1%
p 327
 
0.1%
Other values (3) 582
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 120834
54.3%
Uppercase Letter 101766
45.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 96769
80.1%
t 4670
 
3.9%
e 4670
 
3.9%
a 4670
 
3.9%
d 4670
 
3.9%
y 4670
 
3.9%
p 327
 
0.3%
w 194
 
0.2%
n 194
 
0.2%
Uppercase Letter
ValueCountFrequency (%)
N 96575
94.9%
S 4670
 
4.6%
U 327
 
0.3%
D 194
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 222600
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 96769
43.5%
N 96575
43.4%
S 4670
 
2.1%
t 4670
 
2.1%
e 4670
 
2.1%
a 4670
 
2.1%
d 4670
 
2.1%
y 4670
 
2.1%
U 327
 
0.1%
p 327
 
0.1%
Other values (3) 582
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 222600
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 96769
43.5%
N 96575
43.4%
S 4670
 
2.1%
t 4670
 
2.1%
e 4670
 
2.1%
a 4670
 
2.1%
d 4670
 
2.1%
y 4670
 
2.1%
U 327
 
0.1%
p 327
 
0.1%
Other values (3) 582
 
0.3%

acetohexamide
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
No
101765 
Steady
 
1

Length

Max length6
Median length2
Mean length2.0000393
Min length2

Characters and Unicode

Total characters203536
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 101765
> 99.9%
Steady 1
 
< 0.1%

Length

2023-03-28T10:30:37.883871image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-28T10:30:38.226617image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
no 101765
> 99.9%
steady 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N 101765
50.0%
o 101765
50.0%
S 1
 
< 0.1%
t 1
 
< 0.1%
e 1
 
< 0.1%
a 1
 
< 0.1%
d 1
 
< 0.1%
y 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 101770
50.0%
Uppercase Letter 101766
50.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 101765
> 99.9%
t 1
 
< 0.1%
e 1
 
< 0.1%
a 1
 
< 0.1%
d 1
 
< 0.1%
y 1
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
N 101765
> 99.9%
S 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 203536
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 101765
50.0%
o 101765
50.0%
S 1
 
< 0.1%
t 1
 
< 0.1%
e 1
 
< 0.1%
a 1
 
< 0.1%
d 1
 
< 0.1%
y 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 203536
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 101765
50.0%
o 101765
50.0%
S 1
 
< 0.1%
t 1
 
< 0.1%
e 1
 
< 0.1%
a 1
 
< 0.1%
d 1
 
< 0.1%
y 1
 
< 0.1%

glipizide
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
No
89080 
Steady
11356 
Up
 
770
Down
 
560

Length

Max length6
Median length2
Mean length2.457363
Min length2

Characters and Unicode

Total characters250076
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowSteady
4th rowNo
5th rowSteady

Common Values

ValueCountFrequency (%)
No 89080
87.5%
Steady 11356
 
11.2%
Up 770
 
0.8%
Down 560
 
0.6%

Length

2023-03-28T10:30:38.349772image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-28T10:30:38.525386image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
no 89080
87.5%
steady 11356
 
11.2%
up 770
 
0.8%
down 560
 
0.6%

Most occurring characters

ValueCountFrequency (%)
o 89640
35.8%
N 89080
35.6%
S 11356
 
4.5%
t 11356
 
4.5%
e 11356
 
4.5%
a 11356
 
4.5%
d 11356
 
4.5%
y 11356
 
4.5%
U 770
 
0.3%
p 770
 
0.3%
Other values (3) 1680
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 148310
59.3%
Uppercase Letter 101766
40.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 89640
60.4%
t 11356
 
7.7%
e 11356
 
7.7%
a 11356
 
7.7%
d 11356
 
7.7%
y 11356
 
7.7%
p 770
 
0.5%
w 560
 
0.4%
n 560
 
0.4%
Uppercase Letter
ValueCountFrequency (%)
N 89080
87.5%
S 11356
 
11.2%
U 770
 
0.8%
D 560
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 250076
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 89640
35.8%
N 89080
35.6%
S 11356
 
4.5%
t 11356
 
4.5%
e 11356
 
4.5%
a 11356
 
4.5%
d 11356
 
4.5%
y 11356
 
4.5%
U 770
 
0.3%
p 770
 
0.3%
Other values (3) 1680
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 250076
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 89640
35.8%
N 89080
35.6%
S 11356
 
4.5%
t 11356
 
4.5%
e 11356
 
4.5%
a 11356
 
4.5%
d 11356
 
4.5%
y 11356
 
4.5%
U 770
 
0.3%
p 770
 
0.3%
Other values (3) 1680
 
0.7%

glyburide
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
No
91116 
Steady
9274 
Up
 
812
Down
 
564

Length

Max length6
Median length2
Mean length2.3756068
Min length2

Characters and Unicode

Total characters241756
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 91116
89.5%
Steady 9274
 
9.1%
Up 812
 
0.8%
Down 564
 
0.6%

Length

2023-03-28T10:30:38.668446image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-28T10:30:38.845607image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
no 91116
89.5%
steady 9274
 
9.1%
up 812
 
0.8%
down 564
 
0.6%

Most occurring characters

ValueCountFrequency (%)
o 91680
37.9%
N 91116
37.7%
S 9274
 
3.8%
t 9274
 
3.8%
e 9274
 
3.8%
a 9274
 
3.8%
d 9274
 
3.8%
y 9274
 
3.8%
U 812
 
0.3%
p 812
 
0.3%
Other values (3) 1692
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 139990
57.9%
Uppercase Letter 101766
42.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 91680
65.5%
t 9274
 
6.6%
e 9274
 
6.6%
a 9274
 
6.6%
d 9274
 
6.6%
y 9274
 
6.6%
p 812
 
0.6%
w 564
 
0.4%
n 564
 
0.4%
Uppercase Letter
ValueCountFrequency (%)
N 91116
89.5%
S 9274
 
9.1%
U 812
 
0.8%
D 564
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 241756
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 91680
37.9%
N 91116
37.7%
S 9274
 
3.8%
t 9274
 
3.8%
e 9274
 
3.8%
a 9274
 
3.8%
d 9274
 
3.8%
y 9274
 
3.8%
U 812
 
0.3%
p 812
 
0.3%
Other values (3) 1692
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 241756
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 91680
37.9%
N 91116
37.7%
S 9274
 
3.8%
t 9274
 
3.8%
e 9274
 
3.8%
a 9274
 
3.8%
d 9274
 
3.8%
y 9274
 
3.8%
U 812
 
0.3%
p 812
 
0.3%
Other values (3) 1692
 
0.7%

tolbutamide
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
No
101743 
Steady
 
23

Length

Max length6
Median length2
Mean length2.000904
Min length2

Characters and Unicode

Total characters203624
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 101743
> 99.9%
Steady 23
 
< 0.1%

Length

2023-03-28T10:30:38.988860image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-28T10:30:39.182621image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
no 101743
> 99.9%
steady 23
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N 101743
50.0%
o 101743
50.0%
S 23
 
< 0.1%
t 23
 
< 0.1%
e 23
 
< 0.1%
a 23
 
< 0.1%
d 23
 
< 0.1%
y 23
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 101858
50.0%
Uppercase Letter 101766
50.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 101743
99.9%
t 23
 
< 0.1%
e 23
 
< 0.1%
a 23
 
< 0.1%
d 23
 
< 0.1%
y 23
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
N 101743
> 99.9%
S 23
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 203624
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 101743
50.0%
o 101743
50.0%
S 23
 
< 0.1%
t 23
 
< 0.1%
e 23
 
< 0.1%
a 23
 
< 0.1%
d 23
 
< 0.1%
y 23
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 203624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 101743
50.0%
o 101743
50.0%
S 23
 
< 0.1%
t 23
 
< 0.1%
e 23
 
< 0.1%
a 23
 
< 0.1%
d 23
 
< 0.1%
y 23
 
< 0.1%

pioglitazone
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
No
94438 
Steady
 
6976
Up
 
234
Down
 
118

Length

Max length6
Median length2
Mean length2.2765167
Min length2

Characters and Unicode

Total characters231672
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 94438
92.8%
Steady 6976
 
6.9%
Up 234
 
0.2%
Down 118
 
0.1%

Length

2023-03-28T10:30:39.323822image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-28T10:30:39.492072image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
no 94438
92.8%
steady 6976
 
6.9%
up 234
 
0.2%
down 118
 
0.1%

Most occurring characters

ValueCountFrequency (%)
o 94556
40.8%
N 94438
40.8%
S 6976
 
3.0%
t 6976
 
3.0%
e 6976
 
3.0%
a 6976
 
3.0%
d 6976
 
3.0%
y 6976
 
3.0%
U 234
 
0.1%
p 234
 
0.1%
Other values (3) 354
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 129906
56.1%
Uppercase Letter 101766
43.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 94556
72.8%
t 6976
 
5.4%
e 6976
 
5.4%
a 6976
 
5.4%
d 6976
 
5.4%
y 6976
 
5.4%
p 234
 
0.2%
w 118
 
0.1%
n 118
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
N 94438
92.8%
S 6976
 
6.9%
U 234
 
0.2%
D 118
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 231672
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 94556
40.8%
N 94438
40.8%
S 6976
 
3.0%
t 6976
 
3.0%
e 6976
 
3.0%
a 6976
 
3.0%
d 6976
 
3.0%
y 6976
 
3.0%
U 234
 
0.1%
p 234
 
0.1%
Other values (3) 354
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 231672
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 94556
40.8%
N 94438
40.8%
S 6976
 
3.0%
t 6976
 
3.0%
e 6976
 
3.0%
a 6976
 
3.0%
d 6976
 
3.0%
y 6976
 
3.0%
U 234
 
0.1%
p 234
 
0.1%
Other values (3) 354
 
0.2%

rosiglitazone
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
No
95401 
Steady
 
6100
Up
 
178
Down
 
87

Length

Max length6
Median length2
Mean length2.2414755
Min length2

Characters and Unicode

Total characters228106
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 95401
93.7%
Steady 6100
 
6.0%
Up 178
 
0.2%
Down 87
 
0.1%

Length

2023-03-28T10:30:39.654134image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-28T10:30:39.822511image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
no 95401
93.7%
steady 6100
 
6.0%
up 178
 
0.2%
down 87
 
0.1%

Most occurring characters

ValueCountFrequency (%)
o 95488
41.9%
N 95401
41.8%
S 6100
 
2.7%
t 6100
 
2.7%
e 6100
 
2.7%
a 6100
 
2.7%
d 6100
 
2.7%
y 6100
 
2.7%
U 178
 
0.1%
p 178
 
0.1%
Other values (3) 261
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 126340
55.4%
Uppercase Letter 101766
44.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 95488
75.6%
t 6100
 
4.8%
e 6100
 
4.8%
a 6100
 
4.8%
d 6100
 
4.8%
y 6100
 
4.8%
p 178
 
0.1%
w 87
 
0.1%
n 87
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
N 95401
93.7%
S 6100
 
6.0%
U 178
 
0.2%
D 87
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 228106
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 95488
41.9%
N 95401
41.8%
S 6100
 
2.7%
t 6100
 
2.7%
e 6100
 
2.7%
a 6100
 
2.7%
d 6100
 
2.7%
y 6100
 
2.7%
U 178
 
0.1%
p 178
 
0.1%
Other values (3) 261
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 228106
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 95488
41.9%
N 95401
41.8%
S 6100
 
2.7%
t 6100
 
2.7%
e 6100
 
2.7%
a 6100
 
2.7%
d 6100
 
2.7%
y 6100
 
2.7%
U 178
 
0.1%
p 178
 
0.1%
Other values (3) 261
 
0.1%

acarbose
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
No
101458 
Steady
 
295
Up
 
10
Down
 
3

Length

Max length6
Median length2
Mean length2.0116542
Min length2

Characters and Unicode

Total characters204718
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 101458
99.7%
Steady 295
 
0.3%
Up 10
 
< 0.1%
Down 3
 
< 0.1%

Length

2023-03-28T10:30:39.999366image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-28T10:30:40.159317image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
no 101458
99.7%
steady 295
 
0.3%
up 10
 
< 0.1%
down 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
o 101461
49.6%
N 101458
49.6%
S 295
 
0.1%
t 295
 
0.1%
e 295
 
0.1%
a 295
 
0.1%
d 295
 
0.1%
y 295
 
0.1%
U 10
 
< 0.1%
p 10
 
< 0.1%
Other values (3) 9
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 102952
50.3%
Uppercase Letter 101766
49.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 101461
98.6%
t 295
 
0.3%
e 295
 
0.3%
a 295
 
0.3%
d 295
 
0.3%
y 295
 
0.3%
p 10
 
< 0.1%
w 3
 
< 0.1%
n 3
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
N 101458
99.7%
S 295
 
0.3%
U 10
 
< 0.1%
D 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 204718
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 101461
49.6%
N 101458
49.6%
S 295
 
0.1%
t 295
 
0.1%
e 295
 
0.1%
a 295
 
0.1%
d 295
 
0.1%
y 295
 
0.1%
U 10
 
< 0.1%
p 10
 
< 0.1%
Other values (3) 9
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 204718
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 101461
49.6%
N 101458
49.6%
S 295
 
0.1%
t 295
 
0.1%
e 295
 
0.1%
a 295
 
0.1%
d 295
 
0.1%
y 295
 
0.1%
U 10
 
< 0.1%
p 10
 
< 0.1%
Other values (3) 9
 
< 0.1%

miglitol
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
No
101728 
Steady
 
31
Down
 
5
Up
 
2

Length

Max length6
Median length2
Mean length2.0013167
Min length2

Characters and Unicode

Total characters203666
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 101728
> 99.9%
Steady 31
 
< 0.1%
Down 5
 
< 0.1%
Up 2
 
< 0.1%

Length

2023-03-28T10:30:40.315569image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-28T10:30:40.487404image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
no 101728
> 99.9%
steady 31
 
< 0.1%
down 5
 
< 0.1%
up 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
o 101733
50.0%
N 101728
49.9%
S 31
 
< 0.1%
t 31
 
< 0.1%
e 31
 
< 0.1%
a 31
 
< 0.1%
d 31
 
< 0.1%
y 31
 
< 0.1%
D 5
 
< 0.1%
w 5
 
< 0.1%
Other values (3) 9
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 101900
50.0%
Uppercase Letter 101766
50.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 101733
99.8%
t 31
 
< 0.1%
e 31
 
< 0.1%
a 31
 
< 0.1%
d 31
 
< 0.1%
y 31
 
< 0.1%
w 5
 
< 0.1%
n 5
 
< 0.1%
p 2
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
N 101728
> 99.9%
S 31
 
< 0.1%
D 5
 
< 0.1%
U 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 203666
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 101733
50.0%
N 101728
49.9%
S 31
 
< 0.1%
t 31
 
< 0.1%
e 31
 
< 0.1%
a 31
 
< 0.1%
d 31
 
< 0.1%
y 31
 
< 0.1%
D 5
 
< 0.1%
w 5
 
< 0.1%
Other values (3) 9
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 203666
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 101733
50.0%
N 101728
49.9%
S 31
 
< 0.1%
t 31
 
< 0.1%
e 31
 
< 0.1%
a 31
 
< 0.1%
d 31
 
< 0.1%
y 31
 
< 0.1%
D 5
 
< 0.1%
w 5
 
< 0.1%
Other values (3) 9
 
< 0.1%

troglitazone
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
No
101763 
Steady
 
3

Length

Max length6
Median length2
Mean length2.0001179
Min length2

Characters and Unicode

Total characters203544
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 101763
> 99.9%
Steady 3
 
< 0.1%

Length

2023-03-28T10:30:40.634080image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-28T10:30:40.790346image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
no 101763
> 99.9%
steady 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N 101763
50.0%
o 101763
50.0%
S 3
 
< 0.1%
t 3
 
< 0.1%
e 3
 
< 0.1%
a 3
 
< 0.1%
d 3
 
< 0.1%
y 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 101778
50.0%
Uppercase Letter 101766
50.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 101763
> 99.9%
t 3
 
< 0.1%
e 3
 
< 0.1%
a 3
 
< 0.1%
d 3
 
< 0.1%
y 3
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
N 101763
> 99.9%
S 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 203544
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 101763
50.0%
o 101763
50.0%
S 3
 
< 0.1%
t 3
 
< 0.1%
e 3
 
< 0.1%
a 3
 
< 0.1%
d 3
 
< 0.1%
y 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 203544
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 101763
50.0%
o 101763
50.0%
S 3
 
< 0.1%
t 3
 
< 0.1%
e 3
 
< 0.1%
a 3
 
< 0.1%
d 3
 
< 0.1%
y 3
 
< 0.1%

tolazamide
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
No
101727 
Steady
 
38
Up
 
1

Length

Max length6
Median length2
Mean length2.0014936
Min length2

Characters and Unicode

Total characters203684
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 101727
> 99.9%
Steady 38
 
< 0.1%
Up 1
 
< 0.1%

Length

2023-03-28T10:30:40.927303image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-28T10:30:41.086063image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
no 101727
> 99.9%
steady 38
 
< 0.1%
up 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N 101727
49.9%
o 101727
49.9%
S 38
 
< 0.1%
t 38
 
< 0.1%
e 38
 
< 0.1%
a 38
 
< 0.1%
d 38
 
< 0.1%
y 38
 
< 0.1%
U 1
 
< 0.1%
p 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 101918
50.0%
Uppercase Letter 101766
50.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 101727
99.8%
t 38
 
< 0.1%
e 38
 
< 0.1%
a 38
 
< 0.1%
d 38
 
< 0.1%
y 38
 
< 0.1%
p 1
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
N 101727
> 99.9%
S 38
 
< 0.1%
U 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 203684
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 101727
49.9%
o 101727
49.9%
S 38
 
< 0.1%
t 38
 
< 0.1%
e 38
 
< 0.1%
a 38
 
< 0.1%
d 38
 
< 0.1%
y 38
 
< 0.1%
U 1
 
< 0.1%
p 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 203684
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 101727
49.9%
o 101727
49.9%
S 38
 
< 0.1%
t 38
 
< 0.1%
e 38
 
< 0.1%
a 38
 
< 0.1%
d 38
 
< 0.1%
y 38
 
< 0.1%
U 1
 
< 0.1%
p 1
 
< 0.1%

examide
Boolean

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size99.5 KiB
False
101766 
ValueCountFrequency (%)
False 101766
100.0%
2023-03-28T10:30:41.212369image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size99.5 KiB
False
101766 
ValueCountFrequency (%)
False 101766
100.0%
2023-03-28T10:30:41.337340image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

insulin
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
No
47383 
Steady
30849 
Down
12218 
Up
11316 

Length

Max length6
Median length2
Mean length3.4526659
Min length2

Characters and Unicode

Total characters351364
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowUp
3rd rowNo
4th rowUp
5th rowSteady

Common Values

ValueCountFrequency (%)
No 47383
46.6%
Steady 30849
30.3%
Down 12218
 
12.0%
Up 11316
 
11.1%

Length

2023-03-28T10:30:41.462311image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-28T10:30:41.649729image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
no 47383
46.6%
steady 30849
30.3%
down 12218
 
12.0%
up 11316
 
11.1%

Most occurring characters

ValueCountFrequency (%)
o 59601
17.0%
N 47383
13.5%
S 30849
8.8%
t 30849
8.8%
e 30849
8.8%
a 30849
8.8%
d 30849
8.8%
y 30849
8.8%
D 12218
 
3.5%
w 12218
 
3.5%
Other values (3) 34850
9.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 249598
71.0%
Uppercase Letter 101766
29.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 59601
23.9%
t 30849
12.4%
e 30849
12.4%
a 30849
12.4%
d 30849
12.4%
y 30849
12.4%
w 12218
 
4.9%
n 12218
 
4.9%
p 11316
 
4.5%
Uppercase Letter
ValueCountFrequency (%)
N 47383
46.6%
S 30849
30.3%
D 12218
 
12.0%
U 11316
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 351364
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 59601
17.0%
N 47383
13.5%
S 30849
8.8%
t 30849
8.8%
e 30849
8.8%
a 30849
8.8%
d 30849
8.8%
y 30849
8.8%
D 12218
 
3.5%
w 12218
 
3.5%
Other values (3) 34850
9.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 351364
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 59601
17.0%
N 47383
13.5%
S 30849
8.8%
t 30849
8.8%
e 30849
8.8%
a 30849
8.8%
d 30849
8.8%
y 30849
8.8%
D 12218
 
3.5%
w 12218
 
3.5%
Other values (3) 34850
9.9%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
No
101060 
Steady
 
692
Up
 
8
Down
 
6

Length

Max length6
Median length2
Mean length2.0273176
Min length2

Characters and Unicode

Total characters206312
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 101060
99.3%
Steady 692
 
0.7%
Up 8
 
< 0.1%
Down 6
 
< 0.1%

Length

2023-03-28T10:30:41.812855image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-28T10:30:41.988339image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
no 101060
99.3%
steady 692
 
0.7%
up 8
 
< 0.1%
down 6
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
o 101066
49.0%
N 101060
49.0%
S 692
 
0.3%
t 692
 
0.3%
e 692
 
0.3%
a 692
 
0.3%
d 692
 
0.3%
y 692
 
0.3%
U 8
 
< 0.1%
p 8
 
< 0.1%
Other values (3) 18
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 104546
50.7%
Uppercase Letter 101766
49.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 101066
96.7%
t 692
 
0.7%
e 692
 
0.7%
a 692
 
0.7%
d 692
 
0.7%
y 692
 
0.7%
p 8
 
< 0.1%
w 6
 
< 0.1%
n 6
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
N 101060
99.3%
S 692
 
0.7%
U 8
 
< 0.1%
D 6
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 206312
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 101066
49.0%
N 101060
49.0%
S 692
 
0.3%
t 692
 
0.3%
e 692
 
0.3%
a 692
 
0.3%
d 692
 
0.3%
y 692
 
0.3%
U 8
 
< 0.1%
p 8
 
< 0.1%
Other values (3) 18
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 206312
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 101066
49.0%
N 101060
49.0%
S 692
 
0.3%
t 692
 
0.3%
e 692
 
0.3%
a 692
 
0.3%
d 692
 
0.3%
y 692
 
0.3%
U 8
 
< 0.1%
p 8
 
< 0.1%
Other values (3) 18
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
No
101753 
Steady
 
13

Length

Max length6
Median length2
Mean length2.000511
Min length2

Characters and Unicode

Total characters203584
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 101753
> 99.9%
Steady 13
 
< 0.1%

Length

2023-03-28T10:30:42.141160image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-28T10:30:42.302784image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
no 101753
> 99.9%
steady 13
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N 101753
50.0%
o 101753
50.0%
S 13
 
< 0.1%
t 13
 
< 0.1%
e 13
 
< 0.1%
a 13
 
< 0.1%
d 13
 
< 0.1%
y 13
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 101818
50.0%
Uppercase Letter 101766
50.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 101753
99.9%
t 13
 
< 0.1%
e 13
 
< 0.1%
a 13
 
< 0.1%
d 13
 
< 0.1%
y 13
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
N 101753
> 99.9%
S 13
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 203584
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 101753
50.0%
o 101753
50.0%
S 13
 
< 0.1%
t 13
 
< 0.1%
e 13
 
< 0.1%
a 13
 
< 0.1%
d 13
 
< 0.1%
y 13
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 203584
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 101753
50.0%
o 101753
50.0%
S 13
 
< 0.1%
t 13
 
< 0.1%
e 13
 
< 0.1%
a 13
 
< 0.1%
d 13
 
< 0.1%
y 13
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
No
101765 
Steady
 
1

Length

Max length6
Median length2
Mean length2.0000393
Min length2

Characters and Unicode

Total characters203536
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 101765
> 99.9%
Steady 1
 
< 0.1%

Length

2023-03-28T10:30:42.436516image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-28T10:30:42.601391image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
no 101765
> 99.9%
steady 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N 101765
50.0%
o 101765
50.0%
S 1
 
< 0.1%
t 1
 
< 0.1%
e 1
 
< 0.1%
a 1
 
< 0.1%
d 1
 
< 0.1%
y 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 101770
50.0%
Uppercase Letter 101766
50.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 101765
> 99.9%
t 1
 
< 0.1%
e 1
 
< 0.1%
a 1
 
< 0.1%
d 1
 
< 0.1%
y 1
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
N 101765
> 99.9%
S 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 203536
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 101765
50.0%
o 101765
50.0%
S 1
 
< 0.1%
t 1
 
< 0.1%
e 1
 
< 0.1%
a 1
 
< 0.1%
d 1
 
< 0.1%
y 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 203536
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 101765
50.0%
o 101765
50.0%
S 1
 
< 0.1%
t 1
 
< 0.1%
e 1
 
< 0.1%
a 1
 
< 0.1%
d 1
 
< 0.1%
y 1
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
No
101764 
Steady
 
2

Length

Max length6
Median length2
Mean length2.0000786
Min length2

Characters and Unicode

Total characters203540
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 101764
> 99.9%
Steady 2
 
< 0.1%

Length

2023-03-28T10:30:42.900540image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-28T10:30:43.056753image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
no 101764
> 99.9%
steady 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N 101764
50.0%
o 101764
50.0%
S 2
 
< 0.1%
t 2
 
< 0.1%
e 2
 
< 0.1%
a 2
 
< 0.1%
d 2
 
< 0.1%
y 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 101774
50.0%
Uppercase Letter 101766
50.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 101764
> 99.9%
t 2
 
< 0.1%
e 2
 
< 0.1%
a 2
 
< 0.1%
d 2
 
< 0.1%
y 2
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
N 101764
> 99.9%
S 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 203540
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 101764
50.0%
o 101764
50.0%
S 2
 
< 0.1%
t 2
 
< 0.1%
e 2
 
< 0.1%
a 2
 
< 0.1%
d 2
 
< 0.1%
y 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 203540
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 101764
50.0%
o 101764
50.0%
S 2
 
< 0.1%
t 2
 
< 0.1%
e 2
 
< 0.1%
a 2
 
< 0.1%
d 2
 
< 0.1%
y 2
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
No
101765 
Steady
 
1

Length

Max length6
Median length2
Mean length2.0000393
Min length2

Characters and Unicode

Total characters203536
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 101765
> 99.9%
Steady 1
 
< 0.1%

Length

2023-03-28T10:30:43.189519image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-28T10:30:43.377609image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
no 101765
> 99.9%
steady 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N 101765
50.0%
o 101765
50.0%
S 1
 
< 0.1%
t 1
 
< 0.1%
e 1
 
< 0.1%
a 1
 
< 0.1%
d 1
 
< 0.1%
y 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 101770
50.0%
Uppercase Letter 101766
50.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 101765
> 99.9%
t 1
 
< 0.1%
e 1
 
< 0.1%
a 1
 
< 0.1%
d 1
 
< 0.1%
y 1
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
N 101765
> 99.9%
S 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 203536
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 101765
50.0%
o 101765
50.0%
S 1
 
< 0.1%
t 1
 
< 0.1%
e 1
 
< 0.1%
a 1
 
< 0.1%
d 1
 
< 0.1%
y 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 203536
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 101765
50.0%
o 101765
50.0%
S 1
 
< 0.1%
t 1
 
< 0.1%
e 1
 
< 0.1%
a 1
 
< 0.1%
d 1
 
< 0.1%
y 1
 
< 0.1%

change
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
No
54755 
Ch
47011 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters203532
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowCh
3rd rowNo
4th rowCh
5th rowCh

Common Values

ValueCountFrequency (%)
No 54755
53.8%
Ch 47011
46.2%

Length

2023-03-28T10:30:43.502580image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-28T10:30:43.649995image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
no 54755
53.8%
ch 47011
46.2%

Most occurring characters

ValueCountFrequency (%)
N 54755
26.9%
o 54755
26.9%
C 47011
23.1%
h 47011
23.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 101766
50.0%
Lowercase Letter 101766
50.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 54755
53.8%
C 47011
46.2%
Lowercase Letter
ValueCountFrequency (%)
o 54755
53.8%
h 47011
46.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 203532
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 54755
26.9%
o 54755
26.9%
C 47011
23.1%
h 47011
23.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 203532
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 54755
26.9%
o 54755
26.9%
C 47011
23.1%
h 47011
23.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size99.5 KiB
True
78363 
False
23403 
ValueCountFrequency (%)
True 78363
77.0%
False 23403
 
23.0%
2023-03-28T10:30:43.791859image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

readmitted
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.2 KiB
NO
54864 
>30
35545 
<30
11357 

Length

Max length3
Median length2
Mean length2.4608808
Min length2

Characters and Unicode

Total characters250434
Distinct characters6
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNO
2nd row>30
3rd rowNO
4th rowNO
5th rowNO

Common Values

ValueCountFrequency (%)
NO 54864
53.9%
>30 35545
34.9%
<30 11357
 
11.2%

Length

2023-03-28T10:30:43.934503image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-28T10:30:44.090715image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
no 54864
53.9%
30 46902
46.1%

Most occurring characters

ValueCountFrequency (%)
N 54864
21.9%
O 54864
21.9%
3 46902
18.7%
0 46902
18.7%
> 35545
14.2%
< 11357
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 109728
43.8%
Decimal Number 93804
37.5%
Math Symbol 46902
18.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 54864
50.0%
O 54864
50.0%
Decimal Number
ValueCountFrequency (%)
3 46902
50.0%
0 46902
50.0%
Math Symbol
ValueCountFrequency (%)
> 35545
75.8%
< 11357
 
24.2%

Most occurring scripts

ValueCountFrequency (%)
Common 140706
56.2%
Latin 109728
43.8%

Most frequent character per script

Common
ValueCountFrequency (%)
3 46902
33.3%
0 46902
33.3%
> 35545
25.3%
< 11357
 
8.1%
Latin
ValueCountFrequency (%)
N 54864
50.0%
O 54864
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 250434
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 54864
21.9%
O 54864
21.9%
3 46902
18.7%
0 46902
18.7%
> 35545
14.2%
< 11357
 
4.5%

Interactions

2023-03-28T10:30:13.700929image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:27.208196image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:30.290802image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:35.036112image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:39.870039image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:44.445433image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:47.701428image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:51.108893image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:56.172761image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:59.725698image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:30:03.104799image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:30:06.291610image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:30:09.413298image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:30:13.966224image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:27.475375image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:30.533152image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:35.409123image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:40.193174image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:44.664657image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:47.941550image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:51.471627image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:56.567470image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:59.957252image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:30:03.385149image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:30:06.541741image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:30:09.628050image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:30:14.217584image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:27.699632image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:30.789729image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:35.993916image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:40.877346image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:44.893306image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:48.220140image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:51.741054image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:56.888587image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:30:00.208484image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:30:03.607999image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:30:06.793604image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:30:09.865956image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:30:14.463712image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:27.926274image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:31.043785image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:36.381515image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:41.160073image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:45.121718image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:48.441301image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:51.975434image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:57.236787image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:30:00.441719image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:30:03.849657image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:30:07.061337image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:30:10.158401image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:30:14.709275image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:28.123263image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:31.292162image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:36.719837image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:41.688176image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:45.337775image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:48.684376image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:52.279616image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:57.517311image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:30:00.658009image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:30:04.069547image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:30:07.279236image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:30:10.511458image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:30:14.941614image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:28.346817image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:31.787796image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:37.050574image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:42.039891image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:45.598654image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:49.067038image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:52.575601image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:57.747294image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:30:00.898605image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:30:04.283442image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:30:07.532483image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:30:10.839581image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:30:15.215880image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:28.558049image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:32.320374image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:37.568192image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:42.509625image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:45.942643image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:49.292843image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:52.982393image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:57.972245image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:30:01.158145image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:30:04.516517image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:30:07.750345image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:30:11.148753image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:30:15.461224image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:28.764426image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:32.733271image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:37.919251image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:42.895651image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:46.207423image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:49.522159image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:53.366734image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:58.366271image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:30:01.408005image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:30:04.743855image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:30:07.998451image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:30:11.483857image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:30:15.714546image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:28.991440image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:33.068373image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:38.206555image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:43.209023image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:46.457458image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:49.741879image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:53.734459image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:58.594242image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:30:01.664649image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:30:04.963865image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:30:08.230621image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:30:11.834918image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:30:16.039679image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:29.235852image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:33.471297image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:38.543642image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:43.474564image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:46.750561image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:49.959468image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:54.109740image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:58.852832image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:30:01.928965image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:30:05.374551image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:30:08.474306image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:30:12.178004image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:30:16.277048image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:29.424528image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:33.919099image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:38.818846image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:43.720643image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:46.959347image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:50.234668image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:54.481854image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:59.062584image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:30:02.260636image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:30:05.575820image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:30:08.724027image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:30:12.489174image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:30:16.535357image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:29.640481image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:34.361010image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:39.124065image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:43.972051image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:47.190746image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:50.525972image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:54.850159image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:59.293125image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:30:02.544127image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:30:05.798208image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:30:08.958070image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:30:12.816296image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:30:16.746792image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:29.890287image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:34.715974image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:39.542911image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:44.191927image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:47.440065image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:50.810253image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:55.386609image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:29:59.507380image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:30:02.792125image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:30:06.029002image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:30:09.174220image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-28T10:30:13.433649image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-03-28T10:30:44.323850image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
encounter_idpatient_nbradmission_type_iddischarge_disposition_idadmission_source_idtime_in_hospitalnum_lab_proceduresnum_proceduresnum_medicationsnumber_outpatientnumber_emergencynumber_inpatientnumber_diagnosesracegenderageweightpayer_codemedical_specialtymax_glu_serumA1Cresultmetforminrepaglinidenateglinidechlorpropamideglimepirideacetohexamideglipizideglyburidetolbutamidepioglitazonerosiglitazoneacarbosemiglitoltroglitazonetolazamideinsulinglyburide-metforminglipizide-metforminglimepiride-pioglitazonemetformin-rosiglitazonemetformin-pioglitazonechangediabetesMedreadmitted
encounter_id1.0000.544-0.123-0.065-0.051-0.060-0.009-0.0310.1020.1510.1310.0370.2930.0780.0110.0370.0200.2440.1580.1250.0620.0280.0190.0220.0140.0300.0000.0230.0540.0100.0360.0440.0060.0050.0130.0140.1020.0290.0040.0010.0110.0140.1200.0680.073
patient_nbr0.5441.0000.007-0.0460.030-0.0170.027-0.0190.0450.1550.1130.0260.2400.1060.0220.0390.0370.1740.1580.1980.0810.0210.0420.0180.0060.0230.0000.0210.0440.0000.0320.0190.0110.0090.0000.0090.1180.0320.0240.0000.0230.0000.1300.0680.115
admission_type_id-0.1230.0071.0000.021-0.383-0.015-0.2240.2170.0870.030-0.033-0.045-0.1270.0630.0130.0380.0430.1350.2280.3410.0620.0320.0340.0120.0020.0360.0000.0120.0070.0130.0200.0190.0000.0050.0000.0060.0640.0270.0000.0000.0000.0000.0630.0430.044
discharge_disposition_id-0.065-0.0460.0211.0000.0420.2760.0590.0130.1710.0330.0070.0850.1510.0280.0270.0600.0160.0940.0920.0740.0300.0360.0160.0060.0190.0220.0200.0280.0510.0100.0240.0170.0000.0040.0110.0170.0780.0150.0100.0000.0000.0000.0810.0830.120
admission_source_id-0.0510.030-0.3830.0421.0000.0030.136-0.205-0.0630.0240.1040.0560.1060.0740.0120.0350.0310.0810.1810.3630.0640.0290.0180.0070.0000.0160.0000.0070.0190.0040.0170.0210.0000.0000.0000.0000.0410.0170.0000.0000.0000.0000.0230.0180.056
time_in_hospital-0.060-0.017-0.0150.2760.0031.0000.3370.1870.465-0.013-0.0010.0920.2370.0130.0280.0430.0100.0330.0720.0290.0390.0280.0240.0050.0030.0250.0190.0370.0330.0000.0230.0210.0070.0090.0130.0000.0790.0030.0050.0000.0000.0000.1150.0700.048
num_lab_procedures-0.0090.027-0.2240.0590.1360.3371.0000.0230.252-0.0240.0060.0410.1690.0410.0170.0230.0380.0470.0960.2430.1490.0370.0200.0060.0000.0190.0040.0240.0200.0060.0180.0110.0000.0000.0000.0000.0730.0060.0080.0000.0000.0010.0700.0430.032
num_procedures-0.031-0.0190.2170.013-0.2050.1870.0231.0000.352-0.024-0.046-0.0640.0670.0250.0450.0650.0110.0430.1650.0450.0380.0230.0000.0010.0030.0070.0130.0090.0070.0000.0100.0080.0040.0000.0000.0070.0230.0000.0000.0000.0060.0000.0270.0300.037
num_medications0.1020.0450.0870.171-0.0630.4650.2520.3521.0000.0740.0440.0990.2940.0300.0360.0600.0080.0380.1230.0290.0180.0440.0160.0150.0000.0290.0190.0420.0300.0000.0430.0320.0130.0000.0000.0000.1430.0030.0000.0000.0380.0000.2440.1960.063
number_outpatient0.1510.1550.0300.0330.024-0.013-0.024-0.0240.0741.0000.1770.1560.1130.0120.0000.0040.0190.0240.0110.0130.0090.0070.0000.0000.0000.0000.0000.0000.0050.0000.0000.0000.0000.0000.0000.0000.0180.0000.0000.0000.0000.0000.0150.0010.028
number_emergency0.1310.113-0.0330.0070.104-0.0010.006-0.0460.0440.1771.0000.2220.0920.0040.0000.0270.0000.0340.0510.0000.0000.0000.0000.0210.0000.0090.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0170.0200.0000.0000.0000.0000.0150.0070.029
number_inpatient0.0370.026-0.0450.0850.0560.0920.041-0.0640.0990.1560.2221.0000.1360.0140.0080.0490.0140.0290.0250.0280.0220.0320.0000.0000.0000.0000.0000.0120.0200.0000.0110.0080.0070.0030.0000.0000.0440.0000.0000.0000.0000.0000.0170.0180.130
number_diagnoses0.2930.240-0.1270.1510.1060.2370.1690.0670.2940.1130.0920.1361.0000.0630.0000.1310.0220.0790.1380.0550.0530.0460.0220.0350.0060.0100.0000.0130.0240.0000.0100.0080.0000.0000.0000.0090.0780.0120.0000.0050.0000.0050.0570.0320.082
race0.0780.1060.0630.0280.0740.0130.0410.0250.0300.0120.0040.0140.0631.0000.0540.0850.0360.0870.0940.0400.0320.0120.0160.0100.0030.0140.0000.0140.0170.0000.0150.0060.0070.0000.0000.0000.0420.0180.0000.0000.0280.0000.0210.0220.037
gender0.0110.0220.0130.0270.0120.0280.0170.0450.0360.0000.0000.0080.0000.0541.0000.0780.0270.0610.0790.0000.0160.0000.0000.0000.0000.0000.0000.0190.0230.0000.0040.0110.0070.0040.0040.0030.0000.0000.0050.0000.0020.0000.0140.0150.013
age0.0370.0390.0380.0600.0350.0430.0230.0650.0600.0040.0270.0490.1310.0850.0781.0000.0260.1530.2520.0360.1060.0660.0290.0080.0030.0240.0000.0370.0500.0140.0300.0260.0020.0040.0000.0000.0680.0100.0000.0000.0000.0000.0560.0440.038
weight0.0200.0370.0430.0160.0310.0100.0380.0110.0080.0190.0000.0140.0220.0360.0270.0261.0000.0540.0510.0230.0170.0140.0000.0040.0000.0070.0000.0130.0040.0000.0190.0040.0000.0000.0000.0000.0550.0000.0000.0000.0000.0000.0480.0360.035
payer_code0.2440.1740.1350.0940.0810.0330.0470.0430.0380.0240.0340.0290.0790.0870.0610.1530.0541.0000.1080.0800.0670.0440.0250.0130.0030.0330.0000.0170.0400.0000.0310.0150.0100.0000.0000.0000.1310.0390.0270.0050.0150.0150.1480.0950.049
medical_specialty0.1580.1580.2280.0920.1810.0720.0960.1650.1230.0110.0510.0250.1380.0940.0790.2520.0510.1081.0000.1060.0990.0650.0630.0000.0140.0440.0000.0320.0390.0000.0230.0380.0000.0360.0000.0000.1080.0300.0000.0000.0000.0130.1040.0670.077
max_glu_serum0.1250.1980.3410.0740.3630.0290.2430.0450.0290.0130.0000.0280.0550.0400.0000.0360.0230.0800.1061.0000.0430.0190.0100.0090.0000.0190.0000.0110.0060.0130.0110.0030.0020.0000.0000.0000.0470.0090.0000.0000.0000.0000.0570.0500.015
A1Cresult0.0620.0810.0620.0300.0640.0390.1490.0380.0180.0090.0000.0220.0530.0320.0160.1060.0170.0670.0990.0431.0000.0420.0200.0010.0000.0200.0000.0230.0170.0000.0110.0100.0120.0000.0000.0100.0800.0000.0060.0000.0040.0000.1150.0960.018
metformin0.0280.0210.0320.0360.0290.0280.0370.0230.0440.0070.0000.0320.0460.0120.0000.0660.0140.0440.0650.0190.0421.0000.0090.0130.0030.0280.0000.0490.0930.0050.0340.0610.0130.0080.0000.0080.0320.0120.0000.0000.0000.0410.3290.2700.022
repaglinide0.0190.0420.0340.0160.0180.0240.0200.0000.0160.0000.0000.0000.0220.0160.0000.0290.0000.0250.0630.0100.0200.0091.0000.0000.0000.0030.0000.0100.0140.0000.0150.0060.0120.0080.0000.0000.0180.0030.0000.0000.0000.0000.0780.0680.016
nateglinide0.0220.0180.0120.0060.0070.0050.0060.0010.0150.0000.0210.0000.0350.0100.0000.0080.0040.0130.0000.0090.0010.0130.0001.0000.0000.0090.0000.0070.0110.0000.0200.0090.0000.0050.0000.0000.0040.0040.0000.0000.0000.0000.0550.0450.000
chlorpropamide0.0140.0060.0020.0190.0000.0030.0000.0030.0000.0000.0000.0000.0060.0030.0000.0030.0000.0030.0140.0000.0000.0030.0000.0001.0000.0000.0000.0020.0000.0000.0000.0000.0000.0000.0000.0000.0100.0000.0000.0000.0000.0000.0120.0150.004
glimepiride0.0300.0230.0360.0220.0160.0250.0190.0070.0290.0000.0090.0000.0100.0140.0000.0240.0070.0330.0440.0190.0200.0280.0030.0090.0001.0000.0000.0420.0400.0000.0260.0250.0100.0130.0050.0000.0100.0040.0000.0000.0000.0000.1440.1270.007
acetohexamide0.0000.0000.0000.0200.0000.0190.0040.0130.0190.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
glipizide0.0230.0210.0120.0280.0070.0370.0240.0090.0420.0000.0000.0120.0130.0140.0190.0370.0130.0170.0320.0110.0230.0490.0100.0070.0020.0420.0001.0000.0620.0020.0290.0270.0220.0140.0000.0000.0340.0150.0000.0000.0000.0000.2090.2060.015
glyburide0.0540.0440.0070.0510.0190.0330.0200.0070.0300.0050.0000.0200.0240.0170.0230.0500.0040.0400.0390.0060.0170.0930.0140.0110.0000.0400.0000.0621.0000.0000.0160.0250.0070.0000.0000.0000.0540.0040.0000.0000.0000.0000.1910.1870.004
tolbutamide0.0100.0000.0130.0100.0040.0000.0060.0000.0000.0000.0000.0000.0000.0000.0000.0140.0000.0000.0000.0130.0000.0050.0000.0000.0000.0000.0000.0020.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0070.000
pioglitazone0.0360.0320.0200.0240.0170.0230.0180.0100.0430.0000.0000.0110.0100.0150.0040.0300.0190.0310.0230.0110.0110.0340.0150.0200.0000.0260.0000.0290.0160.0001.0000.0370.0070.0000.0000.0000.0090.0180.0000.0000.0000.0100.2030.1520.011
rosiglitazone0.0440.0190.0190.0170.0210.0210.0110.0080.0320.0000.0000.0080.0080.0060.0110.0260.0040.0150.0380.0030.0100.0610.0060.0090.0000.0250.0000.0270.0250.0000.0371.0000.0020.0000.0030.0000.0130.0020.0000.0000.0000.0000.1960.1410.013
acarbose0.0060.0110.0000.0000.0000.0070.0000.0040.0130.0000.0000.0070.0000.0070.0070.0020.0000.0100.0000.0020.0120.0130.0120.0000.0000.0100.0000.0220.0070.0000.0070.0021.0000.0010.0000.0000.0110.0040.0000.0000.0000.0000.0460.0300.012
miglitol0.0050.0090.0050.0040.0000.0090.0000.0000.0000.0000.0000.0030.0000.0000.0040.0040.0000.0000.0360.0000.0000.0080.0080.0050.0000.0130.0000.0140.0000.0000.0000.0000.0011.0000.0000.0000.0040.0000.0000.0000.0000.0000.0140.0090.005
troglitazone0.0130.0000.0000.0110.0000.0130.0000.0000.0000.0000.0000.0000.0000.0000.0040.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0050.0000.0000.0000.0000.0000.0030.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0030.0000.000
tolazamide0.0140.0090.0060.0170.0000.0000.0000.0070.0000.0000.0000.0000.0090.0000.0030.0000.0000.0000.0000.0000.0100.0080.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0080.0000.0000.0000.0000.0000.0000.0100.002
insulin0.1020.1180.0640.0780.0410.0790.0730.0230.1430.0180.0170.0440.0780.0420.0000.0680.0550.1310.1080.0470.0800.0320.0180.0040.0100.0100.0000.0340.0540.0000.0090.0130.0110.0040.0000.0081.0000.0050.0000.0000.0030.0000.6410.5850.050
glyburide-metformin0.0290.0320.0270.0150.0170.0030.0060.0000.0030.0000.0200.0000.0120.0180.0000.0100.0000.0390.0300.0090.0000.0120.0030.0040.0000.0040.0000.0150.0040.0000.0180.0020.0040.0000.0000.0000.0051.0000.0300.0000.0000.0000.0430.0450.004
glipizide-metformin0.0040.0240.0000.0100.0000.0050.0080.0000.0000.0000.0000.0000.0000.0000.0050.0000.0000.0270.0000.0000.0060.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0301.0000.0000.0000.0000.0070.0040.001
glimepiride-pioglitazone0.0010.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0050.0000.0000.0000.0000.0050.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.000
metformin-rosiglitazone0.0110.0230.0000.0000.0000.0000.0000.0060.0380.0000.0000.0000.0000.0280.0020.0000.0000.0150.0000.0000.0040.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0030.0000.0000.0001.0000.0000.0000.0000.000
metformin-pioglitazone0.0140.0000.0000.0000.0000.0000.0010.0000.0000.0000.0000.0000.0050.0000.0000.0000.0000.0150.0130.0000.0000.0410.0000.0000.0000.0000.0000.0000.0000.0000.0100.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.000
change0.1200.1300.0630.0810.0230.1150.0700.0270.2440.0150.0150.0170.0570.0210.0140.0560.0480.1480.1040.0570.1150.3290.0780.0550.0120.1440.0000.2090.1910.0000.2030.1960.0460.0140.0030.0000.6410.0430.0070.0000.0000.0001.0000.5060.046
diabetesMed0.0680.0680.0430.0830.0180.0700.0430.0300.1960.0010.0070.0180.0320.0220.0150.0440.0360.0950.0670.0500.0960.2700.0680.0450.0150.1270.0000.2060.1870.0070.1520.1410.0300.0090.0000.0100.5850.0450.0040.0000.0000.0000.5061.0000.061
readmitted0.0730.1150.0440.1200.0560.0480.0320.0370.0630.0280.0290.1300.0820.0370.0130.0380.0350.0490.0770.0150.0180.0220.0160.0000.0040.0070.0000.0150.0040.0000.0110.0130.0120.0050.0000.0020.0500.0040.0010.0000.0000.0000.0460.0611.000

Missing values

2023-03-28T10:30:17.861845image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-03-28T10:30:25.021759image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

encounter_idpatient_nbrracegenderageweightadmission_type_iddischarge_disposition_idadmission_source_idtime_in_hospitalpayer_codemedical_specialtynum_lab_proceduresnum_proceduresnum_medicationsnumber_outpatientnumber_emergencynumber_inpatientdiag_1diag_2diag_3number_diagnosesmax_glu_serumA1Cresultmetforminrepaglinidenateglinidechlorpropamideglimepirideacetohexamideglipizideglyburidetolbutamidepioglitazonerosiglitazoneacarbosemiglitoltroglitazonetolazamideexamidecitogliptoninsulinglyburide-metforminglipizide-metforminglimepiride-pioglitazonemetformin-rosiglitazonemetformin-pioglitazonechangediabetesMedreadmitted
022783928222157CaucasianFemale[0-10)?62511?Pediatrics-Endocrinology4101000250.83??1NoneNoneNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNO
114919055629189CaucasianFemale[10-20)?1173??59018000276250.012559NoneNoneNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoUpNoNoNoNoNoChYes>30
26441086047875AfricanAmericanFemale[20-30)?1172??11513201648250V276NoneNoneNoNoNoNoNoNoSteadyNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoYesNO
350036482442376CaucasianMale[30-40)?1172??441160008250.434037NoneNoneNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoUpNoNoNoNoNoChYesNO
41668042519267CaucasianMale[40-50)?1171??51080001971572505NoneNoneNoNoNoNoNoNoSteadyNoNoNoNoNoNoNoNoNoNoSteadyNoNoNoNoNoChYesNO
53575482637451CaucasianMale[50-60)?2123??316160004144112509NoneNoneNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoSteadyNoNoNoNoNoNoYes>30
65584284259809CaucasianMale[60-70)?3124??70121000414411V457NoneNoneSteadyNoNoNoSteadyNoNoNoNoNoNoNoNoNoNoNoNoSteadyNoNoNoNoNoChYesNO
763768114882984CaucasianMale[70-80)?1175??730120004284922508NoneNoneNoNoNoNoNoNoNoSteadyNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoYes>30
81252248330783CaucasianFemale[80-90)?21413??68228000398427388NoneNoneNoNoNoNoNoNoSteadyNoNoNoNoNoNoNoNoNoNoSteadyNoNoNoNoNoChYesNO
91573863555939CaucasianFemale[90-100)?33412?InternalMedicine333180004341984868NoneNoneNoNoNoNoNoNoNoNoNoNoSteadyNoNoNoNoNoNoSteadyNoNoNoNoNoChYesNO
encounter_idpatient_nbrracegenderageweightadmission_type_iddischarge_disposition_idadmission_source_idtime_in_hospitalpayer_codemedical_specialtynum_lab_proceduresnum_proceduresnum_medicationsnumber_outpatientnumber_emergencynumber_inpatientdiag_1diag_2diag_3number_diagnosesmax_glu_serumA1Cresultmetforminrepaglinidenateglinidechlorpropamideglimepirideacetohexamideglipizideglyburidetolbutamidepioglitazonerosiglitazoneacarbosemiglitoltroglitazonetolazamideexamidecitogliptoninsulinglyburide-metforminglipizide-metforminglimepiride-pioglitazonemetformin-rosiglitazonemetformin-pioglitazonechangediabetesMedreadmitted
101756443842070140199494OtherFemale[60-70)?1172MD?466171119965854039NoneNoneNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoSteadyNoNoNoNoNoNoYes>30
101757443842136181593374CaucasianFemale[70-80)?1175??211160014915185119NoneNoneNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoSteadyNoNoNoNoNoNoYesNO
101758443842340120975314CaucasianFemale[80-90)?1175MC?7612201029283049NoneNoneNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoUpNoNoNoNoNoChYesNO
10175944384277886472243CaucasianMale[80-90)?1171MC?10153004357842507NoneNoneNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoUpNoNoNoNoNoChYesNO
10176044384717650375628AfricanAmericanFemale[60-70)?1176DM?451253123454384129NoneNoneNoNoNoNoNoNoNoNoNoNoSteadyNoNoNoNoNoNoDownNoNoNoNoNoChYes>30
101761443847548100162476AfricanAmericanMale[70-80)?1373MC?51016000250.132914589None>8SteadyNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoDownNoNoNoNoNoChYes>30
10176244384778274694222AfricanAmericanFemale[80-90)?1455MC?333180015602767879NoneNoneNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoSteadyNoNoNoNoNoNoYesNO
10176344385414841088789CaucasianMale[70-80)?1171MC?53091003859029613NoneNoneSteadyNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoDownNoNoNoNoNoChYesNO
10176444385716631693671CaucasianFemale[80-90)?23710MCSurgery-General452210019962859989NoneNoneNoNoNoNoNoNoSteadyNoNoSteadyNoNoNoNoNoNoNoUpNoNoNoNoNoChYesNO
101765443867222175429310CaucasianMale[70-80)?1176??13330005305307879NoneNoneNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNO